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2518 Skill Chains 312b4d53
ASecurity**Production AI agents from recipes, not from scratch.** This is the primary way to use Skene: pick a recipe, copy-paste the chain, and deploy. **Skill Chains first** — these 36 recipes are the dishes; they’re built from a library of **765 skills** (the ingredients). No ML expertise required.
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[](https://www.skillsdirectory.com/skills/tools-only-2518-skill-chains-312b4d53)# Skill Chain Cookbook
**Production AI agents from recipes, not from scratch.** This is the primary way to use Skene: pick a recipe, copy-paste the chain, and deploy. **Skill Chains first** — these 36 recipes are the dishes; they’re built from a library of **765 skills** (the ingredients). No ML expertise required.
## 36 Ready-to-Use Recipes
Each recipe in this cookbook is a proven skill chain you can deploy immediately. Every recipe includes:
- **Use case** — What problem it solves
- **Skills** — Which skills to chain together
- **ROI** — Expected time/cost savings
- **Step-by-step instructions** — Copy-paste examples
- **Expected outcomes** — What success looks like
---
## 📋 Quick Navigation
### 💼 Sales & Revenue
- [Recipe 1: Sales Deal Qualification Pipeline](#recipe-1-sales-deal-qualification-pipeline)
- [Recipe 2: Financial Intelligence Dashboard](#recipe-2-financial-intelligence-dashboard)
- [Recipe 3: Product-Led Sales Handoff](#recipe-3-product-led-sales-handoff)
- [Recipe 4: Competitive Intelligence Automation](#recipe-4-competitive-intelligence-automation)
### 🤝 Customer Success & Support
- [Recipe 5: Customer Churn Prevention Pipeline](#recipe-5-customer-churn-prevention-pipeline)
- [Recipe 6: AI Support Deflection System](#recipe-6-ai-support-deflection-system)
- [Recipe 7: Customer Education Platform](#recipe-7-customer-education-platform)
- [Recipe 8: AI Ops Conversation Pipeline](#recipe-8-ai-ops-conversation-pipeline)
- [Recipe 9: Customer Onboarding Automation](#recipe-9-customer-onboarding-automation)
- [Recipe 10: Support Ticket Triage & Resolution](#recipe-10-support-ticket-triage--resolution)
### 🚀 Growth & Marketing
- [Recipe 11: Growth Optimization Engine](#recipe-11-growth-optimization-engine)
- [Recipe 12: Content Marketing Automation](#recipe-12-content-marketing-automation)
- [Recipe 13: Freemium Conversion Optimization](#recipe-13-freemium-conversion-optimization)
- [Recipe 14: Usage-Based Pricing Engine](#recipe-14-usage-based-pricing-engine)
- [Recipe 15: Community-Led Growth Engine](#recipe-15-community-led-growth-engine)
- [Recipe 16: Multi-Platform Content Distribution](#recipe-16-multi-platform-content-distribution)
- [Recipe 17: Pricing & Packaging Optimization](#recipe-17-pricing--packaging-optimization)
- [Recipe 18: Product Analytics Intelligence](#recipe-18-product-analytics-intelligence)
### 🔧 Product & Engineering
- [Recipe 19: Developer Experience Onboarding](#recipe-19-developer-experience-onboarding)
- [Recipe 20: Product Experimentation Engine](#recipe-20-product-experimentation-engine)
- [Recipe 21: API Lifecycle Management](#recipe-21-api-lifecycle-management)
- [Recipe 22: Security Code Review Automation](#recipe-22-security-code-review-automation)
- [Recipe 23: Superpowers Development Workflow](#recipe-23-superpowers-development-workflow)
### 🎨 Brand & Content
- [Recipe 24: Brand Consistency Engine](#recipe-24-brand-consistency-engine)
### ⚙️ Operations & Compliance
- [Recipe 25: Employee Onboarding Automation](#recipe-25-employee-onboarding-automation)
- [Recipe 26: Data Quality Automation](#recipe-26-data-quality-automation)
- [Recipe 27: Compliance Automation Hub](#recipe-27-compliance-automation-hub)
- [Recipe 28: E-commerce Revenue Optimization](#recipe-28-e-commerce-revenue-optimization)
- [Recipe 29: Partnership Ecosystem Automation](#recipe-29-partnership-ecosystem-automation)
- [Recipe 30: People Ops Talent Intelligence](#recipe-30-people-ops-talent-intelligence)
- [Recipe 31: Data Ops Experimentation Pipeline](#recipe-31-data-ops-experimentation-pipeline)
- [Recipe 32: Partnership Deal Flow](#recipe-32-partnership-deal-flow)
### 🔬 Research & Strategy
- [Recipe 33: Scientific Research Synthesis Pipeline](#recipe-33-scientific-research-synthesis-pipeline)
- [Recipe 34: Skene Growth Strategy Chain](#recipe-34-skene-growth-strategy-chain)
### 🌐 Community
- [Recipe 35: Community Advocacy Pipeline](#recipe-35-community-advocacy-pipeline)
### 💰 FinOps
- [Recipe 36: FinOps Standalone Dashboard](#recipe-36-finops-standalone-dashboard)
---
## Recipe index by domain
| Domain | Recipe numbers |
|--------|----------------|
| **Sales & RevOps** | 1, 2, 3, 4 |
| **Customer Success & Support** | 5, 6, 7, 8, 9, 10 |
| **Growth & Marketing** | 11, 12, 13, 14, 15, 16, 17, 18 |
| **Product & Engineering** | 19, 20, 21, 22, 23 |
| **Brand & Content** | 24 |
| **Operations & Compliance** | 25, 26, 27, 28, 29, 30, 31, 32 |
| **Research & Strategy** | 33, 34 |
| **Community** | 35 |
| **FinOps** | 36 |
---
## Recipe 1: Sales Deal Qualification Pipeline
**Use Case:** Automatically qualify, score, and route leads with recommended next actions
**Skills:** 5 chained skills
**Time:** 30-60 seconds per lead (was 2-3 hours)
**ROI:** $20K-$40K/month for 50-lead sales team
### The Chain
```
lead_qualification → opportunity_scoring → deal_inspection →
next_best_action → content_recommender
```
### How It Works
1. **lead_qualification** — Applies MEDDIC/BANT framework, determines if lead is qualified
2. **opportunity_scoring** — Scores qualified leads on fit, urgency, budget
3. **deal_inspection** — Analyzes deal health, identifies risks
4. **next_best_action** — Recommends specific actions for rep
5. **content_recommender** — Suggests relevant case studies, decks
### Setup Instructions
#### Step 1: Install RevOps Skills
```bash
npx skills-directory install --target all --domain revops
```
#### Step 2: Configure Lead Qualification Entry Point
```json
{
"skill": "lead_qualification",
"input": {
"leadId": "{{trigger.leadId}}",
"framework": "MEDDIC",
"sources": ["CRM", "enrichment_data", "website_activity"]
},
"exit_routing": {
"qualified": "opportunity_scoring",
"nurture": "nurture_campaign",
"disqualified": "archive_lead"
}
}
```
#### Step 3: Chain to Opportunity Scoring
```json
{
"skill": "opportunity_scoring",
"input": {
"opportunityId": "{{previous.opportunityId}}",
"qualificationData": "{{previous.output}}",
"scoringModel": "weighted"
},
"exit_routing": {
"high_score": "deal_inspection",
"medium_score": "next_best_action",
"low_score": "nurture_campaign"
}
}
```
#### Step 4: Add Deal Inspection
```json
{
"skill": "deal_inspection",
"input": {
"dealId": "{{previous.dealId}}",
"depth": "comprehensive"
},
"exit_routing": {
"healthy": "next_best_action",
"at_risk": "escalation_manager",
"blocked": "deal_surgery"
}
}
```
#### Step 5: Route to Next Best Action
```json
{
"skill": "next_best_action",
"input": {
"context": "{{chain.all_previous_outputs}}",
"repProfile": "{{user.profile}}",
"priority": "close_deal"
},
"exit_routing": {
"action_recommended": "content_recommender",
"needs_manager": "escalation_manager"
}
}
```
#### Step 6: Recommend Content
```json
{
"skill": "content_recommender",
"input": {
"dealContext": "{{chain.context}}",
"buyerPersona": "{{lead.persona}}",
"dealStage": "{{deal.stage}}"
},
"output": "send_to_rep"
}
```
### Testing the Chain
```bash
# Test with sample lead data
curl -X POST http://localhost:3000/api/chains/sales-qualification \
-H "Content-Type: application/json" \
-d '{
"leadId": "test-lead-123",
"leadData": {
"company": "Acme Corp",
"revenue": "$50M",
"employees": 250,
"industry": "SaaS"
}
}'
```
### Expected Output
```json
{
"qualified": true,
"score": 85,
"tier": "high_value",
"risks": [],
"nextActions": ["Schedule discovery call", "Send ROI calculator", "Introduce solutions engineer"],
"recommendedContent": [
"Case Study: Similar company in SaaS",
"ROI Calculator: Enterprise tier",
"Demo Video: Platform overview"
],
"timeline": "Move to demo within 5 days"
}
```
### Monitoring & Metrics
Track these KPIs:
- **Lead-to-opportunity rate:** Target > 25%
- **Time saved per lead:** Target 90%+ reduction
- **Opportunity quality:** Track close rates
- **Rep satisfaction:** Survey reps on lead quality
### Expected Outcomes
- **Week 1:** Chain deployed, processing 10-20 leads
- **Week 2:** Reps report 50%+ time savings on qualification
- **Month 1:** 25%+ lead-to-opportunity conversion rate
- **Month 3:** $25K+ value delivered in time savings
---
## Recipe 2: Financial Intelligence Dashboard
**Use Case:** Real-time CFO dashboard with automated board reporting
**Skills:** 5 chained skills
**Time:** Real-time updates vs monthly 40-hour manual process
**ROI:** $50K+/month in finance team time
### The Chain
```
arr_waterfall → burn_rate_monitor → magic_number →
investor_metrics → scenario_planner
```
### How It Works
1. **arr_waterfall** — Tracks ARR movements (new, expansion, churn, contraction)
2. **burn_rate_monitor** — Monitors cash burn and runway
3. **magic_number** — Calculates sales efficiency
4. **investor_metrics** — Compiles key metrics (CAC, LTV, Rule of 40)
5. **scenario_planner** — Models "what if" scenarios
### Setup Instructions
#### Step 1: Install FinOps Skills
```bash
npx skills-directory install --target all --domain finops
```
#### Step 2: Configure ARR Waterfall (Real-time)
```json
{
"skill": "arr_waterfall",
"trigger": "data_change",
"input": {
"period": "current_month",
"sources": ["billing", "crm", "contracts"],
"granularity": "daily"
},
"output_to": "burn_rate_monitor"
}
```
#### Step 3: Monitor Burn Rate
```json
{
"skill": "burn_rate_monitor",
"input": {
"arrData": "{{previous.arr}}",
"expenses": "{{accounting.expenses}}",
"runway_threshold": 12
},
"alerts": {
"runway_below_12_months": "immediate",
"burn_increasing_20_percent": "warning"
},
"output_to": "magic_number"
}
```
#### Step 4: Calculate Sales Efficiency
```json
{
"skill": "magic_number",
"input": {
"newARR": "{{arr_waterfall.new}}",
"salesMarketing": "{{expenses.sales_marketing}}",
"period": "quarter"
},
"benchmark": 1.0,
"output_to": "investor_metrics"
}
```
#### Step 5: Compile Investor Metrics
```json
{
"skill": "investor_metrics",
"input": {
"allData": "{{chain.all_previous}}",
"includeMetrics": [
"ARR",
"Net Revenue Retention",
"CAC",
"LTV",
"LTV:CAC Ratio",
"Magic Number",
"Burn Multiple",
"Rule of 40",
"Gross Margin"
]
},
"output_to": "scenario_planner"
}
```
#### Step 6: Enable Scenario Planning
```json
{
"skill": "scenario_planner",
"input": {
"baselineData": "{{previous.metrics}}",
"scenarios": [
{
"name": "aggressive_growth",
"assumptions": { "headcount_increase": 30, "arr_growth": 100 }
},
{
"name": "profitable_growth",
"assumptions": { "headcount_increase": 15, "arr_growth": 50 }
}
]
}
}
```
### One-Click Board Report
```bash
# Generate board report
npx skills-directory run-chain financial-intelligence \
--output board-report \
--format pdf
```
### Expected Outcomes
- **Day 1:** Dashboard showing real-time metrics
- **Week 1:** CFO using for daily decision-making
- **Month 1:** First board report auto-generated
- **Quarter 1:** 90% reduction in manual financial reporting
---
## Recipe 3: Product-Led Sales Handoff
**Use Case:** Bridge self-serve and sales motions by automatically routing product-qualified leads to sales with full context
**Skills:** 6 chained skills
**Time:** Automated PQL detection vs manual review (2+ hours per lead)
**ROI:** 3x PQL-to-opportunity conversion, $1M-$3M pipeline capture, 40% faster sales cycles
### The Chain
```
pql_scoring → usage_depth_analyzer → expansion_playbook →
handoff_orchestration → cpq_quote_generator → deal_inspection
```
### How It Works
1. **pql_scoring** — Identifies product-qualified leads based on usage patterns and engagement
2. **usage_depth_analyzer** — Analyzes depth of product adoption and power user behaviors
3. **expansion_playbook** — Recommends expansion strategies based on usage patterns
4. **handoff_orchestration** — Coordinates seamless handoff from product to sales
5. **cpq_quote_generator** — Auto-generates quotes based on usage and expansion opportunity
6. **deal_inspection** — Validates deal health and identifies potential blockers
### Setup Instructions
#### Step 1: Install PLG & RevOps Skills
```bash
npx skills-directory install --target all --domain plg revops
```
#### Step 2: Configure PQL Scoring
```json
{
"skill": "pql_scoring",
"trigger": "scheduled",
"frequency": "daily",
"input": {
"accountIds": "{{all_active_accounts}}",
"signals": [
"feature_usage",
"team_size",
"integration_count",
"api_usage",
"storage_consumption"
],
"threshold": 75,
"scoringModel": "product_led"
},
"exit_routing": {
"pql_qualified": "usage_depth_analyzer",
"not_qualified": "nurture_sequence"
}
}
```
#### Step 3: Analyze Usage Depth
```json
{
"skill": "usage_depth_analyzer",
"input": {
"accountId": "{{previous.accountId}}",
"pqlScore": "{{previous.score}}",
"analysisDepth": "comprehensive",
"lookbackWindow": "90_days",
"metrics": ["feature_breadth", "power_user_count", "collaboration_score"]
},
"exit_routing": {
"expansion_ready": "expansion_playbook",
"early_stage": "product_education"
}
}
```
#### Step 4: Generate Expansion Playbook
```json
{
"skill": "expansion_playbook",
"input": {
"accountId": "{{chain.accountId}}",
"usageAnalysis": "{{previous.analysis}}",
"currentPlan": "{{account.plan}}",
"expansionVectors": ["seats", "features", "usage_tier", "enterprise_upgrade"],
"targetARR": "calculate"
},
"exit_routing": {
"playbook_ready": "handoff_orchestration",
"needs_enrichment": "data_enrichment"
}
}
```
#### Step 5: Orchestrate Sales Handoff
```json
{
"skill": "handoff_orchestration",
"input": {
"accountId": "{{chain.accountId}}",
"pqlData": "{{chain.pql_data}}",
"expansionPlan": "{{previous.playbook}}",
"assignmentRules": "territory_based",
"notifyRep": true,
"notifyCustomer": false
},
"exit_routing": {
"assigned_to_sales": "cpq_quote_generator",
"hold_for_timing": "schedule_outreach"
}
}
```
#### Step 6: Generate Quote
```json
{
"skill": "cpq_quote_generator",
"input": {
"accountId": "{{chain.accountId}}",
"expansionPlan": "{{chain.expansion_plan}}",
"discountRules": "apply_pql_discount",
"pricingModel": "usage_based",
"includeOnboarding": true
},
"exit_routing": {
"quote_generated": "deal_inspection",
"needs_approval": "manager_review"
}
}
```
#### Step 7: Inspect Deal Health
```json
{
"skill": "deal_inspection",
"input": {
"dealId": "{{previous.dealId}}",
"depth": "comprehensive",
"checkpoints": ["budget", "authority", "timeline", "competition"],
"riskFactors": true
},
"output": "send_to_rep_with_insights"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/pql-handoff \
-H "Content-Type: application/json" \
-d '{
"accountId": "test-account-789",
"usageData": {
"seats": 15,
"featuresUsed": 12,
"apiCalls": 500000,
"teamCollaboration": "high",
"powerUsers": 5
},
"currentPlan": "pro"
}'
```
### Expected Output
```json
{
"pqlQualified": true,
"pqlScore": 89,
"usageDepth": "high",
"expansionOpportunity": {
"type": "enterprise_upgrade",
"estimatedARR": "$50,000",
"confidence": "high"
},
"assignedTo": "rep_john_smith",
"quoteGenerated": true,
"dealHealth": "healthy",
"recommendedActions": [
"Schedule discovery call within 48 hours",
"Present enterprise features demo",
"Discuss API rate limits and custom integration needs"
],
"timeline": "Close within 30 days"
}
```
### Monitoring & Metrics
Track these KPIs:
- **PQL-to-Opportunity Rate:** Target > 60% (baseline ~20%)
- **PQL-to-Close Rate:** Target > 15% (baseline ~5%)
- **Sales Cycle Length:** Target 30-40 days (baseline 60-90 days)
- **Average Deal Size:** Target $50K+ for enterprise upgrades
- **Rep Satisfaction:** Track rep feedback on PQL quality
### Expected Outcomes
- **Week 1:** PQL detection running, 10-20 accounts identified
- **Month 1:** 3x improvement in PQL-to-opportunity conversion
- **Quarter 1:** $1M-$3M incremental pipeline from product-led motion
- **Quarter 2:** 40% reduction in sales cycle for product-led deals
---
## Recipe 4: Competitive Intelligence Automation
**Use Case:** Automate competitive research, feature tracking, pricing analysis, and battlecard generation
**Skills:** 6 chained skills
**Time:** Real-time competitive intelligence vs 20-40 hours/month manual research
**ROI:** 90% faster competitive analysis, 60% better win rates, $200K+ pipeline impact
### The Chain
```
competitive_intel → competitive_feature_tracker → ai_knowledge_synthesizer →
content_research_writer → battlecard_generator → win_loss_analyzer
```
### How It Works
1. **competitive_intel** — Monitors competitors' websites, announcements, and market moves
2. **competitive_feature_tracker** — Tracks competitive feature releases and updates
3. **ai_knowledge_synthesizer** — Synthesizes competitive intelligence from multiple sources
4. **content_research_writer** — Researches and documents competitive positioning
5. **battlecard_generator** — Creates sales battlecards for competitive situations
6. **win_loss_analyzer** — Analyzes win/loss patterns against specific competitors
### Setup Instructions
#### Step 1: Install RevOps, Marketing & AI Ops Skills
```bash
npx skills-directory install --target all --domain revops marketing ai_ops
```
#### Step 2: Configure Competitive Monitoring
```json
{
"skill": "competitive_intel",
"trigger": "scheduled",
"frequency": "daily",
"input": {
"competitors": ["competitor_a", "competitor_b", "competitor_c"],
"monitoringSources": [
"company_websites",
"blog_posts",
"press_releases",
"product_hunt",
"linkedin",
"job_postings",
"tech_news"
],
"alertCriteria": ["product_launch", "pricing_change", "executive_hire", "funding_news"],
"sentimentTracking": true
},
"exit_routing": {
"updates_found": "competitive_feature_tracker",
"no_changes": "continue_monitoring"
}
}
```
#### Step 3: Track Feature Releases
```json
{
"skill": "competitive_feature_tracker",
"input": {
"competitors": "{{previous.competitors}}",
"competitiveUpdates": "{{previous.updates}}",
"featureCategories": [
"core_functionality",
"integrations",
"enterprise_features",
"pricing_tiers",
"security_compliance"
],
"comparisonMatrix": "auto_update",
"gapAnalysis": true
},
"exit_routing": {
"features_tracked": "ai_knowledge_synthesizer",
"critical_feature": "alert_product_team"
}
}
```
#### Step 4: Synthesize Intelligence
```json
{
"skill": "ai_knowledge_synthesizer",
"input": {
"competitiveData": "{{chain.all_data}}",
"synthesisGoals": [
"market_positioning",
"feature_gaps",
"pricing_strategy",
"target_customers",
"go_to_market_approach"
],
"includeTrends": true,
"confidenceScoring": true,
"citeSources": true
},
"exit_routing": {
"synthesis_complete": "content_research_writer",
"needs_validation": "research_team_review"
}
}
```
#### Step 5: Research Competitive Positioning
```json
{
"skill": "content_research_writer",
"input": {
"topic": "competitive_landscape",
"synthesizedIntel": "{{previous.synthesis}}",
"outputFormat": "structured_analysis",
"includeStrengthsWeaknesses": true,
"targetAudience": "sales_team",
"updateFrequency": "monthly"
},
"exit_routing": {
"research_complete": "battlecard_generator",
"needs_expert_input": "analyst_review"
}
}
```
#### Step 6: Generate Sales Battlecards
```json
{
"skill": "battlecard_generator",
"input": {
"competitor": "{{trigger.competitor}}",
"competitiveIntel": "{{chain.all_intel}}",
"battlecardSections": [
"overview",
"strengths_weaknesses",
"key_differentiators",
"common_objections",
"proof_points",
"customer_stories",
"pricing_comparison",
"talk_tracks"
],
"format": "sales_enablement_tool",
"updateTrigger": "competitive_change"
},
"exit_routing": {
"battlecard_ready": "win_loss_analyzer",
"needs_review": "sales_enablement_review"
}
}
```
#### Step 7: Analyze Win/Loss Patterns
```json
{
"skill": "win_loss_analyzer",
"input": {
"competitorId": "{{chain.competitor}}",
"dealData": "{{crm.closed_deals}}",
"analysisWindow": "90_days",
"winLossFactors": [
"price",
"features",
"implementation_time",
"customer_support",
"integrations",
"brand"
],
"includeQuotes": true,
"recommendationEngine": true
},
"output": "win_loss_insights_report"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/competitive-intel \
-H "Content-Type: application/json" \
-d '{
"competitor": "competitor_a",
"monitoringPeriod": "last_7_days",
"analysisType": "comprehensive"
}'
```
### Expected Output
```json
{
"competitiveUpdatesFound": 5,
"criticalUpdates": 2,
"featuresTracked": 12,
"newFeatures": 3,
"pricingChanges": 1,
"synthesisGenerated": true,
"battlecardsUpdated": 3,
"winRate": {
"vs_competitor_a": "65%",
"vs_competitor_b": "72%",
"vs_competitor_c": "58%"
},
"keyInsights": [
"Competitor A launched enterprise SSO - our feature gap",
"Competitor B raised prices 20% - opportunity for us",
"Competitor C struggling with customer support (source: G2)"
],
"recommendations": [
"Prioritize SSO feature development",
"Target Competitor B's price-sensitive customers",
"Emphasize our support quality in competitive situations"
]
}
```
### Monitoring & Metrics
Track these KPIs:
- **Competitive Updates Detected:** Track volume and criticality
- **Win Rate by Competitor:** Target 60%+ vs each major competitor
- **Battlecard Utilization:** Track sales team usage
- **Time to Intelligence:** Target < 24 hours for critical updates
- **Competitive Losses:** Track and categorize reasons for losses
### Expected Outcomes
- **Week 1:** Competitive monitoring active for 3-5 competitors
- **Month 1:** 90% faster competitive analysis vs manual research
- **Quarter 1:** 60% improvement in win rates against tracked competitors
- **Quarter 2:** $200K+ pipeline impact from competitive insights
---
## Recipe 5: Customer Churn Prevention Pipeline
**Use Case:** Predict churn risk 60-90 days early and trigger intervention playbooks
**Skills:** 4 chained skills
**Time:** Real-time monitoring vs quarterly reviews
**ROI:** $400K+ ARR saved annually
### The Chain
```
health_scoring → churn_prediction → risk_mitigation_playbook →
escalation_manager
```
### How It Works
1. **health_scoring** — Continuously monitors account health signals
2. **churn_prediction** — ML-based prediction of churn risk 60-90 days out
3. **risk_mitigation_playbook** — Executes tailored intervention strategies
4. **escalation_manager** — Alerts CSM and triggers manager involvement if needed
### Setup Instructions
#### Step 1: Install Customer Success Skills
```bash
npx skills-directory install --target all --domain customer_success
```
#### Step 2: Configure Health Scoring (Continuous)
```json
{
"skill": "health_scoring",
"trigger": "scheduled",
"frequency": "daily",
"input": {
"accountIds": "{{all_active_accounts}}",
"signals": [
"product_usage",
"support_tickets",
"nps_score",
"contract_value",
"engagement_metrics"
]
},
"exit_routing": {
"healthy": "log_and_continue",
"at_risk": "churn_prediction",
"critical": "immediate_escalation"
}
}
```
#### Step 3: Chain to Churn Prediction
```json
{
"skill": "churn_prediction",
"input": {
"accountId": "{{previous.accountId}}",
"healthData": "{{previous.healthScore}}",
"historicalData": true,
"predictionWindow": "90_days"
},
"exit_routing": {
"high_risk": "risk_mitigation_playbook",
"medium_risk": "risk_mitigation_playbook",
"low_risk": "monitor_only"
}
}
```
#### Step 4: Execute Risk Mitigation
```json
{
"skill": "risk_mitigation_playbook",
"input": {
"accountId": "{{chain.accountId}}",
"riskLevel": "{{previous.riskScore}}",
"riskFactors": "{{previous.factors}}",
"playbookType": "retention"
},
"actions": [
"Schedule executive business review",
"Conduct product usage analysis",
"Offer training/onboarding refresh",
"Introduce customer success resources"
],
"exit_routing": {
"playbook_started": "escalation_manager",
"needs_custom_plan": "csm_intervention"
}
}
```
#### Step 5: Escalate if Needed
```json
{
"skill": "escalation_manager",
"input": {
"accountId": "{{chain.accountId}}",
"context": "{{chain.all_data}}",
"urgency": "{{previous.riskLevel}}"
},
"notifications": ["Assigned CSM", "CSM Manager", "VP Customer Success"]
}
```
### Monitoring Dashboard
Create a real-time dashboard tracking:
```
┌─────────────────────────────────────────────┐
│ Churn Prevention Dashboard │
├─────────────────────────────────────────────┤
│ Accounts Monitored: 347 │
│ At-Risk (High): 12 │
│ At-Risk (Medium): 28 │
│ Playbooks Active: 18 │
│ ARR at Risk: $450K │
│ ARR Saved (QTD): $180K │
└─────────────────────────────────────────────┘
```
### Expected Outcomes
- **Week 1:** Health scoring running on all accounts
- **Week 2:** First at-risk accounts identified
- **Month 1:** 3-5 accounts saved from churn
- **Quarter 1:** $100K-$200K ARR saved
---
## Recipe 6: AI Support Deflection System
**Use Case:** Automate support ticket triage, classification, and resolution with AI-powered response generation and knowledge gap detection
**Skills:** 6 chained skills
**Time:** < 1 minute automated resolution vs 4-8 hour manual response time
**ROI:** 70% ticket deflection, 50% support cost reduction, $150K-$300K annual savings
### The Chain
```
ai_ticket_classifier → ai_intent_classifier → ai_response_suggester →
support_resolution_suggester → support_kb_gap_finder → support_deflector
```
### How It Works
1. **ai_ticket_classifier** — Automatically classifies tickets by type, priority, and routing
2. **ai_intent_classifier** — Identifies customer intent and underlying issue
3. **ai_response_suggester** — Generates contextual AI-powered response drafts
4. **support_resolution_suggester** — Recommends resolution steps based on historical patterns
5. **support_kb_gap_finder** — Identifies missing knowledge base articles
6. **support_deflector** — Deflects tickets to self-serve resources when appropriate
### Setup Instructions
#### Step 1: Install AI Ops & Support Ops Skills
```bash
npx skills-directory install --target all --domain ai_ops support_ops
```
#### Step 2: Configure AI Ticket Classification
```json
{
"skill": "ai_ticket_classifier",
"trigger": "ticket_created",
"input": {
"ticketId": "{{trigger.ticketId}}",
"ticketContent": "{{trigger.body}}",
"customerContext": "{{customer.history}}",
"classificationTypes": ["bug", "feature_request", "how_to", "billing", "technical"],
"priorityLevels": ["urgent", "high", "normal", "low"]
},
"exit_routing": {
"classified": "ai_intent_classifier",
"needs_human": "escalate_immediately"
}
}
```
#### Step 3: Classify Customer Intent
```json
{
"skill": "ai_intent_classifier",
"input": {
"ticketId": "{{chain.ticketId}}",
"ticketType": "{{previous.classification}}",
"customerMessage": "{{trigger.body}}",
"contextualData": "{{customer.account_data}}",
"intentCategories": [
"resolve_issue",
"get_information",
"request_feature",
"report_bug",
"cancel_service"
]
},
"exit_routing": {
"intent_clear": "ai_response_suggester",
"intent_ambiguous": "clarification_needed"
}
}
```
#### Step 4: Generate AI Response
```json
{
"skill": "ai_response_suggester",
"input": {
"ticketId": "{{chain.ticketId}}",
"intent": "{{previous.intent}}",
"classification": "{{chain.classification}}",
"knowledgeBase": "search",
"tone": "professional_friendly",
"includeLinks": true
},
"exit_routing": {
"response_generated": "support_resolution_suggester",
"kb_gaps_found": "support_kb_gap_finder"
}
}
```
#### Step 5: Suggest Resolution Steps
```json
{
"skill": "support_resolution_suggester",
"input": {
"ticketId": "{{chain.ticketId}}",
"intent": "{{chain.intent}}",
"aiResponse": "{{previous.response}}",
"historicalResolutions": "search_similar",
"resolutionType": ["self_serve", "agent_assisted", "escalated"]
},
"exit_routing": {
"self_serve_possible": "support_deflector",
"needs_agent": "assign_to_agent",
"needs_escalation": "escalate_to_tier2"
}
}
```
#### Step 6: Find Knowledge Base Gaps
```json
{
"skill": "support_kb_gap_finder",
"input": {
"ticketId": "{{chain.ticketId}}",
"ticketType": "{{chain.classification}}",
"searchAttempts": "{{chain.kb_searches}}",
"commonQueries": "analyze",
"gapThreshold": 3
},
"exit_routing": {
"gap_found": "create_kb_article_task",
"no_gap": "support_deflector"
}
}
```
#### Step 7: Deflect to Self-Serve
```json
{
"skill": "support_deflector",
"input": {
"ticketId": "{{chain.ticketId}}",
"aiResponse": "{{chain.ai_response}}",
"resolutionSteps": "{{previous.steps}}",
"kbArticles": "{{chain.relevant_articles}}",
"deflectionConfidence": "{{previous.confidence}}"
},
"output": "send_to_customer_or_agent"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/support-deflection \
-H "Content-Type: application/json" \
-d '{
"ticketId": "test-ticket-001",
"customerMessage": "How do I reset my API key?",
"customerId": "cust_123",
"accountType": "pro"
}'
```
### Expected Output
```json
{
"ticketClassified": true,
"classification": "how_to",
"priority": "normal",
"intent": "get_information",
"aiResponseGenerated": true,
"deflectionPossible": true,
"kbArticles": ["How to Reset Your API Key", "API Security Best Practices"],
"resolutionSteps": [
"Navigate to Settings > API Keys",
"Click 'Regenerate Key'",
"Update key in your application"
],
"deflected": true,
"estimatedResolutionTime": "< 1 minute",
"supportCostSaved": "$25"
}
```
### Monitoring & Metrics
Track these KPIs:
- **Deflection Rate:** Target > 70% of eligible tickets
- **First Response Time:** Target < 1 minute for AI responses
- **Customer Satisfaction:** Target > 4.5/5 for deflected tickets
- **Support Cost Savings:** Target $150K-$300K annually
- **Knowledge Base Coverage:** Track gap identification and article creation
### Expected Outcomes
- **Week 1:** AI classification active on 100% of tickets
- **Month 1:** 70% deflection rate for how-to and informational tickets
- **Quarter 1:** $50K-$100K support cost savings
- **Quarter 2:** 90% coverage in knowledge base, minimal gaps
---
## Recipe 7: Customer Education Platform
**Use Case:** Automate customer education and product adoption with guided onboarding, milestone celebrations, and content curation
**Skills:** 6 chained skills
**Time:** Automated vs manual 1-on-1 training sessions
**ROI:** 45% higher product adoption, 60% faster time-to-value, 70% reduction in support tickets
### The Chain
```
guided_setup_wizard → milestone_celebration → community_content_curator →
email_sequence → feedback_collection → activation_metrics
```
### How It Works
1. **guided_setup_wizard** — Walks users through initial product setup step-by-step
2. **milestone_celebration** — Celebrates user achievements to increase engagement
3. **community_content_curator** — Surfaces relevant community content and best practices
4. **email_sequence** — Sends educational email drips based on user progress
5. **feedback_collection** — Gathers user feedback on educational content
6. **activation_metrics** — Tracks activation progress and identifies stuck users
### Setup Instructions
#### Step 1: Install Customer Success & PLG Skills
```bash
npx skills-directory install --target all --domain customer_success plg
```
#### Step 2: Configure Guided Setup
```json
{
"skill": "guided_setup_wizard",
"trigger": "user_signup",
"input": {
"userId": "{{trigger.userId}}",
"productComplexity": "medium",
"setupSteps": [
{
"step": 1,
"title": "Connect your data source",
"required": true,
"helpContent": "integration-guide.md"
},
{
"step": 2,
"title": "Invite your team",
"required": false,
"helpContent": "collaboration-guide.md"
},
{
"step": 3,
"title": "Create your first dashboard",
"required": true,
"helpContent": "dashboard-tutorial.md"
}
],
"adaptiveGuidance": true,
"allowSkip": false
},
"exit_routing": {
"setup_complete": "milestone_celebration",
"setup_abandoned": "retention_campaign",
"stuck": "intervention_trigger"
}
}
```
#### Step 3: Celebrate Milestones
```json
{
"skill": "milestone_celebration",
"input": {
"userId": "{{chain.userId}}",
"milestone": "{{previous.completed_step}}",
"celebrationTypes": {
"in_app_notification": true,
"confetti_animation": true,
"badge_award": true,
"email_notification": true
},
"nextMilestone": "auto_suggest",
"shareOption": "social_media"
},
"exit_routing": {
"celebrated": "community_content_curator",
"user_inactive": "re_engagement"
}
}
```
#### Step 4: Curate Educational Content
```json
{
"skill": "community_content_curator",
"input": {
"userId": "{{chain.userId}}",
"userProgress": "{{chain.milestones}}",
"contentTypes": [
"video_tutorials",
"how_to_guides",
"use_cases",
"community_posts",
"webinar_recordings"
],
"personalization": {
"industry": "{{user.industry}}",
"useCase": "{{user.use_case}}",
"skillLevel": "{{user.expertise}}"
},
"maxItems": 5
},
"exit_routing": {
"content_delivered": "email_sequence",
"no_relevant_content": "content_creation_request"
}
}
```
#### Step 5: Send Educational Emails
```json
{
"skill": "email_sequence",
"input": {
"userId": "{{chain.userId}}",
"sequenceType": "educational_drip",
"emailSchedule": [
{
"day": 1,
"topic": "Getting Started",
"content": "{{chain.curated_content[0]}}"
},
{
"day": 3,
"topic": "Best Practices",
"content": "{{chain.curated_content[1]}}"
},
{
"day": 7,
"topic": "Advanced Features",
"content": "{{chain.curated_content[2]}}"
}
],
"adaptToProgress": true,
"includeSuccessStories": true
},
"exit_routing": {
"sequence_started": "feedback_collection",
"user_unsubscribed": "in_app_only"
}
}
```
#### Step 6: Collect Feedback
```json
{
"skill": "feedback_collection",
"input": {
"userId": "{{chain.userId}}",
"feedbackTriggers": ["milestone_reached", "content_consumed", "email_opened", "feature_used"],
"feedbackTypes": {
"nps": true,
"content_rating": true,
"feature_request": true,
"help_needed": true
},
"frequency": "milestone_based",
"format": "in_app_modal"
},
"exit_routing": {
"feedback_received": "activation_metrics",
"negative_feedback": "support_escalation"
}
}
```
#### Step 7: Track Activation Metrics
```json
{
"skill": "activation_metrics",
"input": {
"userId": "{{chain.userId}}",
"activationCriteria": [
"completed_setup",
"invited_team_member",
"created_first_artifact",
"used_core_feature_3_times",
"returned_3_days_in_week"
],
"timeWindow": "14_days",
"activationScore": "calculate",
"identifyBlockers": true
},
"output": "activation_report"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/customer-education \
-H "Content-Type: application/json" \
-d '{
"userId": "new-user-456",
"signupDate": "2026-02-01",
"industry": "SaaS",
"useCase": "product_analytics"
}'
```
### Expected Output
```json
{
"status": "success",
"educationPlan": {
"setupProgress": "67%",
"milestonesCompleted": 2,
"milestonesRemaining": 1,
"contentRecommended": 5,
"emailsScheduled": 3,
"feedbackCollected": true,
"activationScore": 72
},
"userEngagement": {
"setupTime": "12 minutes",
"contentViews": 3,
"emailOpenRate": "85%",
"npsScore": 9,
"blockers": []
},
"nextActions": ["Complete final setup step", "Invite team member", "Explore advanced features"]
}
```
### Monitoring & Metrics
Track these KPIs:
- **Activation Rate:** Target 60%+ within 14 days (baseline ~30%)
- **Time to Value:** Target < 2 days (baseline ~7 days)
- **Content Engagement:** Track views, clicks, completion rates
- **Support Ticket Reduction:** Target 70% fewer "how do I..." tickets
- **NPS from Educated Users:** Target 40+ points
### Expected Outcomes
- **Week 1:** Guided setup deployed, 100+ users onboarded
- **Month 1:** 45% higher activation rate vs. control group
- **Quarter 1:** 60% faster time-to-value, 70% support ticket reduction
- **Quarter 2:** $100K+ saved in customer success time
---
## Recipe 8: AI Ops Conversation Pipeline
**Use Case:** Classify, route, suggest responses, and summarize for support or sales conversations
**Skills:** 4 chained skills (AI Ops)
**Time:** Seconds per ticket; deflected volume
**ROI:** 30–50% deflection, faster first response, consistent summaries
### The Chain
```
ai_ticket_classifier → ai_intent_classifier → ai_response_suggester →
ai_summarization_agent
```
### How It Works
1. **ticket_classifier** — Categories and prioritizes incoming tickets
2. **intent_classifier** — Detects user intent for routing and context
3. **response_suggester** — Suggests replies or next actions
4. **summarization_agent** — Produces conversation or thread summaries
### Setup
Install domain: `npx skills-directory install --target all --domain ai_ops`. Chain the four skills with exit_routing from each step to the next; final step output to your CRM or support queue.
### Expected Outcomes
- **Week 1:** Pipeline running on sample tickets
- **Month 1:** 30%+ deflection or faster resolution
- **Quarter 1:** Summaries used for coaching and reporting
---
## Recipe 9: Customer Onboarding Automation
```
onboarding_health → guided_setup_wizard → milestone_celebration →
time_to_value → activation_metrics
```
**ROI:** 50% faster time-to-value, 30% higher activation
---
## Recipe 10: Support Ticket Triage & Resolution
```
support_ticket_triage → support_resolution_suggester →
support_kb_gap_finder → support_bug_linker
```
**ROI:** 40% faster resolution, 60% ticket deflection
---
## Recipe 11: Growth Optimization Engine
**Use Case:** Continuous A/B testing and conversion optimization
**Skills:** 6 chained skills
**Time:** 10-15 experiments/month vs 2-4
**ROI:** 15-25% conversion lift
### The Chain
```
signup_flow_cro → page_cro → ab_test_setup →
analytics_tracking → activation_metrics → feature_adoption
```
### How It Works
1. **signup_flow_cro** — Optimizes signup conversion
2. **page_cro** — Optimizes landing page conversion
3. **ab_test_setup** — Configures and launches A/B tests
4. **analytics_tracking** — Tracks all metrics
5. **activation_metrics** — Monitors activation funnel
6. **feature_adoption** — Tracks feature usage
### Setup Instructions
#### Step 1: Install Marketing/PLG Skills
```bash
npx skills-directory install --target all --domain marketing plg
```
#### Step 2: Start with Signup Flow Optimization
```json
{
"skill": "signup_flow_cro",
"input": {
"currentFlow": "{{app.signup_flow}}",
"conversionGoal": "completed_signup",
"optimizationFocus": ["friction_points", "form_fields", "social_proof"]
},
"exit_routing": {
"recommendations_ready": "ab_test_setup"
}
}
```
#### Step 3: Run Parallel Page Optimization
```json
{
"skill": "page_cro",
"input": {
"pages": ["homepage", "pricing", "features"],
"goal": "trial_signup",
"analyze": ["copy", "cta", "layout", "images"]
},
"exit_routing": {
"recommendations_ready": "ab_test_setup"
}
}
```
#### Step 4: Automated A/B Test Setup
```json
{
"skill": "ab_test_setup",
"input": {
"recommendations": "{{previous.all_recommendations}}",
"testPlatform": "optimizely",
"traffic_split": "50/50",
"duration": "2_weeks",
"min_sample_size": 1000
},
"exit_routing": {
"test_launched": "analytics_tracking"
}
}
```
#### Step 5: Track Everything
```json
{
"skill": "analytics_tracking",
"input": {
"events": [
"page_view",
"signup_started",
"signup_completed",
"feature_used",
"trial_converted"
],
"destinations": ["mixpanel", "amplitude", "datawarehouse"]
}
}
```
#### Step 6: Monitor Activation & Adoption
```json
{
"skill": "activation_metrics",
"input": {
"activationDefinition": "{{company.aha_moment}}",
"timeWindow": "7_days"
}
},
{
"skill": "feature_adoption",
"input": {
"features": "{{product.feature_list}}",
"cohorts": ["week_1", "week_2", "month_1"]
}
}
```
### Growth Experimentation Dashboard
```
┌─────────────────────────────────────────────┐
│ Active Experiments │
├─────────────────────────────────────────────┤
│ Signup Flow: 3-step vs 1-step │
│ Status: Running | Day 8 of 14 │
│ Winner: 1-step (+23% conversion) ✓ │
│ │
│ Pricing Page: New layout │
│ Status: Running | Day 3 of 14 │
│ Current: +8% trial signups │
│ │
│ Homepage Hero: Value prop test │
│ Status: Complete │
│ Winner: "Build in days" (+15%) ✓ │
└─────────────────────────────────────────────┘
```
### Expected Outcomes
- **Week 1:** First 3 experiments launched
- **Month 1:** 10-15 tests running simultaneously
- **Quarter 1:** 15-25% average conversion lift
- **Quarter 2:** 4x increase in experiment velocity
---
## Recipe 12: Content Marketing Automation
**Use Case:** End-to-end content creation, distribution, and optimization
**Skills:** 7 chained skills
**Time:** 2-3 hours per piece vs 8-12 hours
**ROI:** 2.5x content volume, 40% traffic increase
### The Chain
```
content_research_writer → copywriting → seo_audit →
social_content_generator → email_sequence →
analytics_tracking → social_listening_analyzer
```
### How It Works
1. **content_research_writer** — Researches topic, generates outline & draft
2. **copywriting** — Refines copy, optimizes for readability
3. **seo_audit** — Ensures SEO best practices
4. **social_content_generator** — Creates social posts
5. **email_sequence** — Generates email promotion sequence
6. **analytics_tracking** — Tracks performance
7. **social_listening_analyzer** — Monitors engagement
### Setup Instructions
#### Step 1: Install Marketing Skills
```bash
npx skills-directory install --target all --domain marketing
```
#### Step 2: Configure Content Research & Writing
```json
{
"skill": "content_research_writer",
"input": {
"topic": "{{user_input.topic}}",
"audience": "{{company.buyer_persona}}",
"contentType": "blog_post",
"targetLength": 1500,
"includeExamples": true
},
"exit_routing": {
"draft_complete": "copywriting"
}
}
```
#### Step 3: Refine Copy
```json
{
"skill": "copywriting",
"input": {
"draft": "{{previous.output}}",
"tone": "professional_friendly",
"optimizeFor": ["clarity", "engagement", "cta_conversion"]
},
"exit_routing": {
"copy_refined": "seo_audit"
}
}
```
#### Step 4: SEO Optimization
```json
{
"skill": "seo_audit",
"input": {
"content": "{{previous.output}}",
"targetKeyword": "{{research.primary_keyword}}",
"checks": ["keyword_density", "meta_description", "headings", "internal_links", "readability"]
},
"exit_routing": {
"seo_optimized": "social_content_generator"
}
}
```
#### Step 5: Generate Social Content
```json
{
"skill": "social_content_generator",
"input": {
"article": "{{chain.final_content}}",
"platforms": ["twitter", "linkedin", "facebook"],
"postsPerPlatform": 3,
"includeImages": true
},
"exit_routing": {
"social_content_ready": "email_sequence"
}
}
```
#### Step 6: Create Email Sequence
```json
{
"skill": "email_sequence",
"input": {
"content": "{{chain.final_content}}",
"sequenceType": "nurture",
"emailCount": 3,
"spacing": "3_days"
}
}
```
#### Step 7: Track & Listen
```json
{
"skill": "analytics_tracking",
"input": {
"contentId": "{{chain.contentId}}",
"metrics": ["views", "time_on_page", "conversions", "social_shares"]
}
},
{
"skill": "social_listening_analyzer",
"input": {
"contentUrl": "{{chain.publish_url}}",
"keywords": "{{chain.target_keywords}}",
"sentiment": true
}
}
```
### Content Pipeline Dashboard
```
┌─────────────────────────────────────────────┐
│ This Month: Content Production │
├─────────────────────────────────────────────┤
│ Published: 22 articles (↑ 2.5x) │
│ Organic Traffic: +43% │
│ Social Engagement: 12K interactions │
│ Email CTR: 4.2% (↑ 0.8%) │
│ Time Saved: 180 hours │
└─────────────────────────────────────────────┘
```
### Expected Outcomes
- **Week 1:** First 3 pieces published
- **Month 1:** 20+ pieces published
- **Quarter 1:** 40% increase in organic traffic
- **Quarter 2:** Content team 3x more productive
---
## Recipe 13: Freemium Conversion Optimization
**Use Case:** Optimize trial-to-paid conversion for freemium products with automated upgrade triggers and self-serve expansion workflows
**Skills:** 6 chained skills
**Time:** Real-time monitoring vs manual quarterly reviews
**ROI:** 35% trial-to-paid lift, 25% faster conversion cycle, $300K-$800K ARR increase
### The Chain
```
pql_scoring → onboarding_health → feature_adoption →
upgrade_trigger → paywall_upgrade_cro → self_serve_expansion
```
### How It Works
1. **pql_scoring** — Scores trial users based on product engagement, feature usage, and behavioral fit
2. **onboarding_health** — Monitors trial health and identifies at-risk users early
3. **feature_adoption** — Tracks which premium features drive conversion decisions
4. **upgrade_trigger** — Identifies optimal moments to surface upgrade prompts
5. **paywall_upgrade_cro** — A/B tests paywall messaging, pricing display, and call-to-action
6. **self_serve_expansion** — Enables frictionless self-service plan upgrades
### Setup Instructions
#### Step 1: Install PLG & Monetization Skills
```bash
npx skills-directory install --target all --domain plg monetization
```
#### Step 2: Configure PQL Scoring
```json
{
"skill": "pql_scoring",
"trigger": "user_event",
"input": {
"userId": "{{trigger.userId}}",
"events": ["feature_used", "time_spent", "invites_sent", "integration_added"],
"scoringModel": "engagement_based",
"threshold": 70
},
"exit_routing": {
"high_score": "onboarding_health",
"low_score": "activation_campaign"
}
}
```
#### Step 3: Monitor Onboarding Health
```json
{
"skill": "onboarding_health",
"input": {
"userId": "{{previous.userId}}",
"pqlScore": "{{previous.score}}",
"trialDaysRemaining": "{{user.trial_days_left}}",
"healthSignals": ["login_frequency", "feature_usage", "team_invites", "setup_completion"]
},
"exit_routing": {
"healthy": "feature_adoption",
"at_risk": "retention_campaign",
"churned": "win_back_campaign"
}
}
```
#### Step 4: Track Feature Adoption
```json
{
"skill": "feature_adoption",
"input": {
"userId": "{{chain.userId}}",
"onboardingData": "{{previous.health_data}}",
"premiumFeatures": [
"advanced_analytics",
"team_collaboration",
"api_access",
"custom_integrations"
],
"trackingWindow": "7_days"
},
"exit_routing": {
"premium_used": "upgrade_trigger",
"basic_only": "feature_education"
}
}
```
#### Step 5: Trigger Upgrade Prompts
```json
{
"skill": "upgrade_trigger",
"input": {
"userId": "{{chain.userId}}",
"adoptionData": "{{previous.feature_usage}}",
"pqlScore": "{{chain.pql_score}}",
"triggerType": "feature_limit",
"timing": "optimal_moment"
},
"exit_routing": {
"trigger_sent": "paywall_upgrade_cro",
"wait": "monitor_only"
}
}
```
#### Step 6: Optimize Paywall Experience
```json
{
"skill": "paywall_upgrade_cro",
"input": {
"userId": "{{chain.userId}}",
"context": "{{chain.all_data}}",
"variants": ["value_focused", "urgency_focused", "social_proof", "comparison_table"],
"testDuration": "14_days"
},
"exit_routing": {
"converted": "self_serve_expansion",
"dismissed": "follow_up_sequence"
}
}
```
#### Step 7: Enable Self-Serve Expansion
```json
{
"skill": "self_serve_expansion",
"input": {
"userId": "{{chain.userId}}",
"selectedPlan": "{{previous.plan_choice}}",
"paymentMethod": "{{user.payment_method}}",
"prorateOption": true
},
"output": "send_to_billing"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/freemium-conversion \
-H "Content-Type: application/json" \
-d '{
"userId": "test-user-123",
"trialData": {
"daysInTrial": 10,
"featureUsage": {
"basic": 15,
"advanced": 3,
"premium": 1
},
"teamSize": 1,
"setupComplete": true
}
}'
```
### Expected Output
```json
{
"converted": true,
"pqlScore": 87,
"onboardingHealth": "healthy",
"premiumFeaturesUsed": ["advanced_analytics"],
"triggerType": "feature_limit",
"paywallVariant": "value_focused",
"selectedPlan": "pro_monthly",
"conversionTime": "Day 11 of 14",
"estimatedLTV": "$2,400",
"timeline": "Converted within optimal window"
}
```
### Monitoring & Metrics
Track these KPIs:
- **Trial-to-Paid Rate:** Target > 30% (baseline ~5-10%)
- **Time to Conversion:** Target < 10 days (baseline ~12 days)
- **Paywall Conversion Rate:** Target > 15%
- **Self-Serve Upgrade Rate:** Track monthly expansion revenue
- **PQL Score Distribution:** Monitor score accuracy vs actual conversions
### Expected Outcomes
- **Week 1:** Chain deployed, monitoring 100+ trial users
- **Month 1:** 35% trial-to-paid improvement vs. control group
- **Quarter 1:** $300K-$800K incremental ARR from improved conversions
- **Quarter 2:** Self-serve expansion driving 20%+ of new ARR
---
## Recipe 14: Usage-Based Pricing Engine
**Use Case:** Implement consumption-based pricing with automated metering, overage prediction, and billing workflows
**Skills:** 6 chained skills
**Time:** Real-time usage tracking vs monthly manual reconciliation
**ROI:** 25% revenue per customer increase, 15% churn reduction, $400K-$1M ARR lift
### The Chain
```
usage_metering → consumption_analyzer → overage_predictor →
dunning_automation → limit_notification → invoice_explainer
```
### How It Works
1. **usage_metering** — Tracks real-time product usage across all billing dimensions
2. **consumption_analyzer** — Analyzes usage patterns, identifies trends and anomalies
3. **overage_predictor** — Predicts when customers will exceed plan limits
4. **dunning_automation** — Automates payment collection and retry logic
5. **limit_notification** — Alerts customers before hitting usage limits
6. **invoice_explainer** — Generates detailed, easy-to-understand invoices
### Setup Instructions
#### Step 1: Install Monetization Skills
```bash
npx skills-directory install --target all --domain monetization
```
#### Step 2: Configure Usage Metering
```json
{
"skill": "usage_metering",
"trigger": "usage_event",
"input": {
"accountId": "{{trigger.accountId}}",
"metricType": "{{trigger.metric}}",
"dimensions": ["api_calls", "storage_gb", "compute_hours", "seats"],
"aggregationWindow": "hourly",
"granularity": "high"
},
"exit_routing": {
"metered": "consumption_analyzer"
}
}
```
#### Step 3: Analyze Consumption Patterns
```json
{
"skill": "consumption_analyzer",
"input": {
"accountId": "{{previous.accountId}}",
"usageData": "{{previous.metrics}}",
"analysisWindow": "30_days",
"compareWith": ["plan_limits", "historical_usage", "cohort_avg"]
},
"exit_routing": {
"within_limits": "monitor_only",
"approaching_limit": "overage_predictor",
"exceeded_limit": "dunning_automation"
}
}
```
#### Step 4: Predict Overage Events
```json
{
"skill": "overage_predictor",
"input": {
"accountId": "{{chain.accountId}}",
"consumptionTrend": "{{previous.trend}}",
"planLimits": "{{account.plan_limits}}",
"predictionHorizon": "7_days",
"confidence": "high"
},
"exit_routing": {
"overage_likely": "limit_notification",
"within_buffer": "monitor_only"
}
}
```
#### Step 5: Send Limit Notifications
```json
{
"skill": "limit_notification",
"input": {
"accountId": "{{chain.accountId}}",
"usagePercent": "{{previous.usage_percent}}",
"estimatedOverage": "{{previous.overage_amount}}",
"notificationTiming": ["75_percent", "90_percent", "100_percent"],
"includeUpgradeOptions": true
},
"exit_routing": {
"notified": "dunning_automation",
"upgraded": "usage_metering"
}
}
```
#### Step 6: Automate Payment Collection
```json
{
"skill": "dunning_automation",
"input": {
"accountId": "{{chain.accountId}}",
"invoiceAmount": "{{chain.total_amount}}",
"paymentMethod": "{{account.payment_method}}",
"retryStrategy": "exponential_backoff",
"maxRetries": 3
},
"exit_routing": {
"payment_successful": "invoice_explainer",
"payment_failed": "billing_alert"
}
}
```
#### Step 7: Generate Invoice Explanation
```json
{
"skill": "invoice_explainer",
"input": {
"accountId": "{{chain.accountId}}",
"usageData": "{{chain.all_usage}}",
"charges": "{{previous.charges}}",
"format": "detailed_breakdown",
"includeComparisons": true
},
"output": "send_to_customer"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/usage-billing \
-H "Content-Type: application/json" \
-d '{
"accountId": "test-account-456",
"usageData": {
"api_calls": 950000,
"storage_gb": 450,
"compute_hours": 1800,
"plan_limit": 1000000
},
"billingPeriod": "2026-02"
}'
```
### Expected Output
```json
{
"metered": true,
"usagePercent": 95,
"overagePredicted": true,
"estimatedOverage": "$250",
"notificationSent": true,
"paymentProcessed": true,
"invoiceGenerated": true,
"breakdown": {
"base_plan": "$99",
"overage_charges": "$250",
"total": "$349"
},
"nextAction": "Monitor usage next cycle"
}
```
### Monitoring & Metrics
Track these KPIs:
- **Revenue Per Customer:** Target 25% increase from usage-based pricing
- **Payment Success Rate:** Target > 95%
- **Overage Notification Effectiveness:** Track upgrade rate after notifications
- **Invoice Dispute Rate:** Target < 2%
- **Customer Satisfaction with Billing:** Track NPS on billing experience
### Expected Outcomes
- **Week 1:** Usage metering deployed, tracking 50+ accounts
- **Month 1:** 25% revenue increase from usage-based charges
- **Quarter 1:** 15% churn reduction due to transparent pricing
- **Quarter 2:** $400K-$1M ARR lift from consumption pricing model
---
## Recipe 15: Community-Led Growth Engine
**Use Case:** Drive organic growth through community engagement, user-generated content, ambassador programs, and event management
**Skills:** 6 chained skills
**Time:** Automated community management vs 40+ hours/week manual effort
**ROI:** 40% organic growth increase, 50% CAC reduction, 3x word-of-mouth referrals
### The Chain
```
community_champion_identifier → community_ambassador_program → community_forum_moderator →
community_ugc_curator → community_event_manager → community_case_study_finder
```
### How It Works
1. **community_champion_identifier** — Identifies power users and community champions based on engagement
2. **community_ambassador_program** — Recruits and manages community ambassadors
3. **community_forum_moderator** — Assists with forum moderation and content review
4. **community_ugc_curator** — Curates and surfaces best user-generated content
5. **community_event_manager** — Plans, promotes, and manages community events
6. **community_case_study_finder** — Identifies and qualifies members for case studies
### Setup Instructions
#### Step 1: Install Community Skills
```bash
npx skills-directory install --target all --domain community
```
#### Step 2: Configure Champion Identification
```json
{
"skill": "community_champion_identifier",
"trigger": "scheduled",
"frequency": "weekly",
"input": {
"communityMembers": "{{all_members}}",
"engagementSignals": [
"posts_created",
"helpful_responses",
"upvotes_received",
"event_attendance",
"referrals_made"
],
"championThreshold": 80,
"lookbackWindow": "90_days"
},
"exit_routing": {
"champions_found": "community_ambassador_program",
"no_new_champions": "engagement_campaign"
}
}
```
#### Step 3: Manage Ambassador Program
```json
{
"skill": "community_ambassador_program",
"input": {
"championIds": "{{previous.champion_list}}",
"programTier": ["bronze", "silver", "gold", "platinum"],
"benefits": ["early_feature_access", "exclusive_events", "swag", "revenue_share"],
"requirements": {
"bronze": "10_quality_posts_per_month",
"silver": "25_posts_plus_1_event",
"gold": "50_posts_plus_3_events",
"platinum": "100_posts_plus_5_events"
},
"autoPromote": true
},
"exit_routing": {
"ambassadors_active": "community_forum_moderator",
"needs_recruiting": "ambassador_recruitment_campaign"
}
}
```
#### Step 4: Assist Forum Moderation
```json
{
"skill": "community_forum_moderator",
"trigger": "post_created",
"input": {
"postId": "{{trigger.postId}}",
"postContent": "{{trigger.content}}",
"authorId": "{{trigger.authorId}}",
"moderationRules": ["no_spam", "no_self_promotion", "respectful_tone", "on_topic"],
"autoModerate": "flag_for_review",
"ambassadorOverride": true
},
"exit_routing": {
"approved": "community_ugc_curator",
"flagged": "human_review",
"spam": "auto_remove"
}
}
```
#### Step 5: Curate User-Generated Content
```json
{
"skill": "community_ugc_curator",
"input": {
"contentSource": "{{previous.approved_posts}}",
"curationCriteria": ["helpfulness", "uniqueness", "engagement", "educational_value"],
"contentTypes": ["tutorials", "use_cases", "tips", "success_stories"],
"featuredThreshold": 90,
"distributionChannels": ["newsletter", "social_media", "blog"]
},
"exit_routing": {
"content_curated": "community_event_manager",
"feature_worthy": "create_blog_post"
}
}
```
#### Step 6: Manage Community Events
```json
{
"skill": "community_event_manager",
"input": {
"eventType": "{{trigger.event_type}}",
"eventFormat": ["webinar", "meetup", "workshop", "conference"],
"ambassadorInvolvement": "required",
"promotionChannels": ["email", "forum", "social"],
"targetAttendance": "auto_calculate",
"followUpSequence": "automated"
},
"exit_routing": {
"event_scheduled": "community_case_study_finder",
"low_registration": "promotion_boost"
}
}
```
#### Step 7: Find Case Study Candidates
```json
{
"skill": "community_case_study_finder",
"input": {
"memberIds": "{{chain.community_members}}",
"qualificationCriteria": [
"product_success_story",
"measurable_results",
"willing_to_share",
"diverse_use_case"
],
"outreachTemplate": "case_study_invitation",
"incentive": "featured_placement",
"targetCaseStudies": 10
},
"output": "case_study_pipeline"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/community-growth \
-H "Content-Type: application/json" \
-d '{
"communitySize": 5000,
"activeMembers": 1200,
"timeWindow": "last_30_days",
"eventType": "monthly_meetup"
}'
```
### Expected Output
```json
{
"championsIdentified": 25,
"ambassadorsActive": 15,
"ambassadorTiers": {
"bronze": 8,
"silver": 4,
"gold": 2,
"platinum": 1
},
"postsModerated": 450,
"spamRemoved": 12,
"ugcCurated": 35,
"featuredContent": 8,
"eventsScheduled": 3,
"eventRegistrations": 180,
"caseStudyCandidates": 12,
"organicGrowthRate": "15% month-over-month",
"referralConversions": 45
}
```
### Monitoring & Metrics
Track these KPIs:
- **Community Growth Rate:** Target 40% organic growth annually
- **Ambassador Engagement:** Target > 80% monthly activity rate
- **UGC Volume:** Target 50+ quality posts per month
- **Event Attendance:** Target 70%+ registration-to-attendance rate
- **Case Study Conversion:** Target 5+ case studies per quarter
### Expected Outcomes
- **Week 1:** Champion identification running, 10-20 champions found
- **Month 1:** 15 active ambassadors driving community engagement
- **Quarter 1:** 40% increase in organic community growth
- **Quarter 2:** 50% CAC reduction through community-led acquisition
---
## Recipe 16: Multi-Platform Content Distribution
**Use Case:** Automate content creation, formatting, and distribution across multiple platforms with AI-powered generation and analytics tracking
**Skills:** 6 chained skills
**Time:** 2-3 hours per content piece vs 8-12 hours manual process
**ROI:** 3x content reach, 50% cost reduction, 80% time savings
### The Chain
```
content_research_writer → ai_content_generator → social_content_generator →
email_sequence → analytics_tracking → social_listening_analyzer
```
### How It Works
1. **content_research_writer** — Researches topics, generates outlines, and creates initial drafts
2. **ai_content_generator** — Generates polished content with AI assistance
3. **social_content_generator** — Creates platform-specific social media posts
4. **email_sequence** — Generates email nurture sequences from content
5. **analytics_tracking** — Tracks performance across all distribution channels
6. **social_listening_analyzer** — Monitors social engagement and sentiment
### Setup Instructions
#### Step 1: Install Marketing Skills
```bash
npx skills-directory install --target all --domain marketing
```
#### Step 2: Configure Content Research
```json
{
"skill": "content_research_writer",
"trigger": "content_request",
"input": {
"topic": "{{trigger.topic}}",
"audience": "{{company.target_audience}}",
"contentType": "blog_post",
"targetLength": 1500,
"researchDepth": "comprehensive",
"includeExamples": true,
"citeSources": true
},
"exit_routing": {
"draft_complete": "ai_content_generator",
"needs_more_research": "research_deeper"
}
}
```
#### Step 3: Generate AI-Powered Content
```json
{
"skill": "ai_content_generator",
"input": {
"draft": "{{previous.draft}}",
"tone": "professional_friendly",
"style": "{{company.brand_voice}}",
"optimizeFor": ["clarity", "engagement", "seo", "readability"],
"includeCallToAction": true,
"targetKeyword": "{{previous.primary_keyword}}"
},
"exit_routing": {
"content_ready": "social_content_generator",
"needs_editing": "human_review"
}
}
```
#### Step 4: Create Social Media Content
```json
{
"skill": "social_content_generator",
"input": {
"article": "{{previous.content}}",
"platforms": ["twitter", "linkedin", "facebook", "instagram"],
"postsPerPlatform": 3,
"includeHashtags": true,
"includeImages": true,
"schedulingStrategy": "optimal_times"
},
"exit_routing": {
"social_ready": "email_sequence",
"needs_images": "image_generation"
}
}
```
#### Step 5: Generate Email Nurture Sequence
```json
{
"skill": "email_sequence",
"input": {
"content": "{{chain.article}}",
"sequenceType": "nurture",
"emailCount": 3,
"spacing": "3_days",
"personalization": "high",
"segmentation": "by_behavior",
"includeUnsubscribe": true
},
"exit_routing": {
"sequence_ready": "analytics_tracking",
"needs_approval": "marketing_review"
}
}
```
#### Step 6: Track Analytics
```json
{
"skill": "analytics_tracking",
"input": {
"contentId": "{{chain.contentId}}",
"trackingEvents": [
"page_view",
"time_on_page",
"social_share",
"email_open",
"email_click",
"conversion"
],
"platforms": "all",
"reportingFrequency": "daily"
},
"exit_routing": {
"tracking_active": "social_listening_analyzer"
}
}
```
#### Step 7: Monitor Social Engagement
```json
{
"skill": "social_listening_analyzer",
"input": {
"contentUrl": "{{chain.publish_url}}",
"keywords": "{{chain.target_keywords}}",
"platforms": ["twitter", "linkedin", "reddit", "hackernews"],
"sentimentAnalysis": true,
"competitorMentions": true,
"alertThreshold": "high_engagement"
},
"output": "engagement_report"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/content-distribution \
-H "Content-Type: application/json" \
-d '{
"topic": "AI-powered workflow automation",
"targetAudience": "B2B SaaS founders",
"contentType": "blog_post",
"distributionChannels": ["blog", "social", "email"]
}'
```
### Expected Output
```json
{
"contentGenerated": true,
"articleUrl": "https://blog.example.com/ai-workflow-automation",
"wordCount": 1500,
"readabilityScore": 65,
"socialPosts": {
"twitter": 3,
"linkedin": 3,
"facebook": 3
},
"emailSequence": {
"emails": 3,
"scheduled": true
},
"analyticsTracking": "active",
"socialListening": "monitoring",
"estimatedReach": "15000+",
"productionTime": "2.5 hours"
}
```
### Monitoring & Metrics
Track these KPIs:
- **Content Production Volume:** Target 3x increase
- **Time per Piece:** Target 70% reduction
- **Cross-Platform Reach:** Target 3x increase
- **Engagement Rate:** Target 25% improvement
- **Content-to-Conversion Rate:** Track ROI per piece
### Expected Outcomes
- **Week 1:** 3 pieces published across all channels
- **Month 1:** 20+ pieces published (vs 8-10 manual)
- **Quarter 1:** 3x content reach, 50% cost reduction
- **Quarter 2:** Content team 3x more productive
---
## Recipe 17: Pricing & Packaging Optimization
```
pricing_strategy → packaging_optimizer → price_experimentation →
upgrade_trigger → consumption_analyzer
```
**ROI:** 15-25% revenue per customer increase
---
## Recipe 18: Product Analytics Intelligence
```
product_analytics → feature_adoption → friction_detector →
pql_scoring → expansion_playbook
```
**ROI:** 2x product-led pipeline generation
---
## Phase 2: High-Confidence Recipes (11-28)
These recipes expand into new domains with 95%+ skill coverage validated against the library.
---
## Recipe 19: Developer Experience Onboarding
**Use Case:** Accelerate developer onboarding with automated API documentation, code samples, sandbox provisioning, and integration health monitoring
**Skills:** 6 chained skills
**Time:** 2-4 hours to first API call vs 4-8 weeks manual process
**ROI:** 60% faster integration time, 40% higher completion rate, 2x API adoption
### The Chain
```
api_onboarding → code_sample_generator → sandbox_manager →
integration_health → error_explainer → changelog_tracker
```
### How It Works
1. **api_onboarding** — Guides developers through API setup, authentication, and first call
2. **code_sample_generator** — Generates working code examples in multiple languages
3. **sandbox_manager** — Provisions isolated sandbox environments for testing
4. **integration_health** — Monitors integration health and identifies issues early
5. **error_explainer** — Provides detailed explanations for API errors
6. **changelog_tracker** — Keeps developers informed of API changes and deprecations
### Setup Instructions
#### Step 1: Install DevEx Skills
```bash
npx skills-directory install --target all --domain devex
```
#### Step 2: Configure API Onboarding
```json
{
"skill": "api_onboarding",
"trigger": "developer_signup",
"input": {
"developerId": "{{trigger.developerId}}",
"apiProduct": "{{trigger.api_product}}",
"experienceLevel": "{{developer.experience}}",
"preferredLanguage": "{{developer.language}}",
"onboardingGoal": "first_successful_call"
},
"exit_routing": {
"setup_complete": "code_sample_generator",
"needs_help": "support_escalation"
}
}
```
#### Step 3: Generate Code Samples
```json
{
"skill": "code_sample_generator",
"input": {
"developerId": "{{chain.developerId}}",
"apiEndpoints": "{{previous.endpoints_selected}}",
"languages": ["python", "javascript", "ruby", "go", "java"],
"includeAuth": true,
"includeErrorHandling": true,
"complexity": "beginner_friendly"
},
"exit_routing": {
"samples_generated": "sandbox_manager",
"needs_customization": "custom_sample_request"
}
}
```
#### Step 4: Provision Sandbox Environment
```json
{
"skill": "sandbox_manager",
"input": {
"developerId": "{{chain.developerId}}",
"sandboxType": "isolated",
"dataSeeding": "sample_data",
"rateLimits": "development_tier",
"duration": "90_days",
"features": "all_api_features"
},
"exit_routing": {
"sandbox_ready": "integration_health",
"provisioning_failed": "retry_or_alert"
}
}
```
#### Step 5: Monitor Integration Health
```json
{
"skill": "integration_health",
"input": {
"developerId": "{{chain.developerId}}",
"sandboxId": "{{previous.sandboxId}}",
"healthChecks": ["api_calls_success_rate", "auth_errors", "rate_limit_hits", "response_times"],
"monitoringWindow": "continuous",
"alertThresholds": {
"error_rate": 10,
"no_activity": "7_days"
}
},
"exit_routing": {
"healthy": "changelog_tracker",
"issues_detected": "error_explainer",
"inactive": "re_engagement_campaign"
}
}
```
#### Step 6: Explain API Errors
```json
{
"skill": "error_explainer",
"input": {
"developerId": "{{chain.developerId}}",
"errorCode": "{{trigger.error_code}}",
"errorContext": "{{trigger.request_context}}",
"explanationDepth": "detailed",
"includeSolutions": true,
"relatedDocs": true
},
"exit_routing": {
"explained": "integration_health",
"needs_support": "developer_support"
}
}
```
#### Step 7: Track API Changes
```json
{
"skill": "changelog_tracker",
"input": {
"developerId": "{{chain.developerId}}",
"integratedEndpoints": "{{chain.endpoints_used}}",
"notificationPreference": "{{developer.notification_preference}}",
"changeTypes": ["breaking", "deprecation", "new_feature", "bug_fix"],
"relevanceFilter": "high"
},
"output": "notify_developer_of_changes"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/devex-onboarding \
-H "Content-Type: application/json" \
-d '{
"developerId": "dev_456",
"apiProduct": "core_api",
"language": "python",
"experienceLevel": "intermediate",
"goal": "integrate_user_auth"
}'
```
### Expected Output
```json
{
"onboardingComplete": true,
"timeToFirstCall": "2 hours",
"codeSamplesGenerated": 5,
"languages": ["python", "javascript", "curl"],
"sandboxProvisioned": true,
"sandboxUrl": "https://sandbox.api.example.com",
"integrationHealth": "healthy",
"apiCallsSuccessRate": 98,
"changelogSubscribed": true,
"nextSteps": [
"Test authentication flow",
"Integrate user profile endpoint",
"Review rate limits for production"
]
}
```
### Monitoring & Metrics
Track these KPIs:
- **Time to First API Call:** Target < 4 hours (baseline 1-2 weeks)
- **Integration Completion Rate:** Target > 70% (baseline ~30%)
- **Sandbox Utilization:** Track active developer engagement
- **Error Rate Reduction:** Target 50% reduction via error explainer
- **API Adoption Rate:** Target 2x increase in active integrations
### Expected Outcomes
- **Week 1:** 50+ developers onboarded through automated flow
- **Month 1:** 60% reduction in time to first successful API call
- **Quarter 1:** 40% increase in integration completion rate
- **Quarter 2:** 2x growth in active API integrations
---
## Recipe 20: Product Experimentation Engine
**Use Case:** Accelerate product experimentation with automated A/B test setup, analysis, and impact measurement
**Skills:** 6 chained skills
**Time:** 10-15 experiments/month vs 2-4 manual experiments
**ROI:** 5x experiment velocity, 25% faster product iteration, 20% conversion lift
### The Chain
```
experimentation → ab_test_setup → analytics_tracking →
feature_adoption → impact_analyzer → friction_detector
```
### How It Works
1. **experimentation** — Designs and configures product experiments
2. **ab_test_setup** — Sets up A/B tests with proper statistical controls
3. **analytics_tracking** — Tracks experiment metrics and user behavior
4. **feature_adoption** — Monitors feature adoption rates and patterns
5. **impact_analyzer** — Analyzes business impact of product changes
6. **friction_detector** — Identifies friction points in user experience
### Setup Instructions
#### Step 1: Install Product Ops & PLG Skills
```bash
npx skills-directory install --target all --domain product_ops plg
```
#### Step 2: Configure Experimentation Framework
```json
{
"skill": "experimentation",
"trigger": "experiment_proposal",
"input": {
"experimentId": "{{trigger.experimentId}}",
"hypothesis": "{{trigger.hypothesis}}",
"targetMetric": "{{trigger.primary_metric}}",
"userSegment": "{{trigger.segment}}",
"experimentType": "ab_test",
"successCriteria": {
"statistical_significance": 0.95,
"minimum_effect_size": 0.05,
"minimum_sample_size": 1000
}
},
"exit_routing": {
"design_complete": "ab_test_setup",
"needs_refinement": "experiment_review"
}
}
```
#### Step 3: Setup A/B Test
```json
{
"skill": "ab_test_setup",
"input": {
"experimentId": "{{chain.experimentId}}",
"variants": ["control", "variant_a", "variant_b"],
"trafficAllocation": {
"control": 34,
"variant_a": 33,
"variant_b": 33
},
"targetingRules": "{{previous.segment}}",
"duration": "14_days",
"rampUpStrategy": "gradual"
},
"exit_routing": {
"test_live": "analytics_tracking",
"configuration_error": "fix_and_retry"
}
}
```
#### Step 4: Track Experiment Metrics
```json
{
"skill": "analytics_tracking",
"input": {
"experimentId": "{{chain.experimentId}}",
"events": [
"experiment_exposure",
"primary_metric_event",
"secondary_metric_event",
"conversion_event"
],
"granularity": "user_level",
"cohortTracking": true,
"realTimeAnalysis": true
},
"exit_routing": {
"tracking_active": "feature_adoption",
"data_quality_issue": "alert_team"
}
}
```
#### Step 5: Monitor Feature Adoption
```json
{
"skill": "feature_adoption",
"input": {
"experimentId": "{{chain.experimentId}}",
"features": "{{previous.new_features}}",
"adoptionMetrics": [
"first_use_rate",
"repeat_use_rate",
"power_user_adoption",
"time_to_adoption"
],
"cohortComparison": true,
"timeWindow": "30_days"
},
"exit_routing": {
"adoption_tracked": "impact_analyzer",
"low_adoption": "friction_detector"
}
}
```
#### Step 6: Analyze Business Impact
```json
{
"skill": "impact_analyzer",
"input": {
"experimentId": "{{chain.experimentId}}",
"businessMetrics": ["revenue_impact", "retention_impact", "ltv_impact", "engagement_impact"],
"statisticalMethod": "bayesian",
"confidenceLevel": 0.95,
"includeSegmentAnalysis": true
},
"exit_routing": {
"significant_winner": "rollout_recommendation",
"inconclusive": "extend_experiment",
"significant_negative": "rollback_recommendation"
}
}
```
#### Step 7: Detect User Friction
```json
{
"skill": "friction_detector",
"input": {
"experimentId": "{{chain.experimentId}}",
"frictionSignals": [
"high_bounce_rate",
"abandoned_flows",
"error_rate",
"support_tickets",
"negative_feedback"
],
"variantComparison": true,
"alertThreshold": "medium"
},
"output": "friction_report_with_recommendations"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/experimentation \
-H "Content-Type: application/json" \
-d '{
"experimentId": "exp_001",
"hypothesis": "Simplified signup flow increases conversion by 15%",
"primaryMetric": "signup_completion_rate",
"userSegment": "new_visitors"
}'
```
### Expected Output
```json
{
"experimentLive": true,
"variants": {
"control": "current_3_step_flow",
"variant_a": "simplified_1_step",
"variant_b": "2_step_with_social"
},
"sampleSize": 3000,
"currentResults": {
"control": "12.5% conversion",
"variant_a": "18.3% conversion (+46%)",
"variant_b": "15.7% conversion (+26%)"
},
"statisticalSignificance": 0.98,
"winner": "variant_a",
"recommendation": "Roll out variant_a to 100% of users",
"estimatedImpact": "$150K annual revenue increase"
}
```
### Monitoring & Metrics
Track these KPIs:
- **Experiment Velocity:** Target 10-15 experiments/month
- **Time to Results:** Target < 14 days per experiment
- **Winner Rate:** Target 40%+ experiments produce winners
- **Impact per Experiment:** Track cumulative revenue/engagement impact
- **Experimentation Culture:** Track % of product decisions backed by experiments
### Expected Outcomes
- **Week 1:** First 3 experiments launched
- **Month 1:** 10-15 experiments running concurrently
- **Quarter 1:** 5x increase in experiment velocity
- **Quarter 2:** 20% cumulative conversion improvement from winning experiments
---
## Recipe 21: API Lifecycle Management
**Use Case:** Manage API versioning, deprecations, and breaking changes with automated migration assistance and developer notifications
**Skills:** 6 chained skills
**Time:** Automated change management vs 40+ hours per API update
**ROI:** 50% reduction in breaking change impact, 70% faster migrations, 90% developer satisfaction
### The Chain
```
api_deprecation → changelog_tracker → migration_assistant →
deprecation_notifier → webhook_tester → integration_health
```
### How It Works
1. **api_deprecation** — Plans and manages API deprecation timelines
2. **changelog_tracker** — Tracks and communicates API changes to developers
3. **migration_assistant** — Provides automated migration guidance and code samples
4. **deprecation_notifier** — Notifies affected customers about upcoming changes
5. **webhook_tester** — Tests webhook integrations before and after changes
6. **integration_health** — Monitors integration health across API versions
### Setup Instructions
#### Step 1: Install DevEx & Product Ops Skills
```bash
npx skills-directory install --target all --domain devex product_ops
```
#### Step 2: Configure API Deprecation Planning
```json
{
"skill": "api_deprecation",
"trigger": "deprecation_proposal",
"input": {
"apiEndpoint": "{{trigger.endpoint}}",
"currentVersion": "{{trigger.version}}",
"deprecationReason": "{{trigger.reason}}",
"timelineMonths": 6,
"migrationPath": "{{trigger.new_endpoint}}",
"breakingChange": true,
"affectedCustomers": "auto_calculate"
},
"exit_routing": {
"plan_approved": "changelog_tracker",
"needs_review": "api_review_board"
}
}
```
#### Step 3: Track and Communicate Changes
```json
{
"skill": "changelog_tracker",
"input": {
"deprecationId": "{{previous.deprecationId}}",
"changeType": "deprecation",
"affectedEndpoints": "{{previous.endpoints}}",
"releaseNotes": "auto_generate",
"changelogFormat": "markdown",
"distributionChannels": ["email", "developer_portal", "slack", "rss"],
"versionTagging": "semver"
},
"exit_routing": {
"changelog_published": "migration_assistant",
"needs_technical_writer": "documentation_review"
}
}
```
#### Step 4: Provide Migration Assistance
```json
{
"skill": "migration_assistant",
"input": {
"deprecationId": "{{chain.deprecationId}}",
"fromVersion": "{{chain.current_version}}",
"toVersion": "{{chain.new_version}}",
"migrationGuides": "auto_generate",
"codeExamples": ["python", "javascript", "ruby", "go"],
"diffVisualization": true,
"interactiveTutorial": true
},
"exit_routing": {
"migration_docs_ready": "deprecation_notifier",
"needs_custom_examples": "engineering_support"
}
}
```
#### Step 5: Notify Affected Customers
```json
{
"skill": "deprecation_notifier",
"input": {
"deprecationId": "{{chain.deprecationId}}",
"affectedCustomers": "{{chain.affected_list}}",
"notificationSchedule": ["180_days", "90_days", "30_days", "7_days"],
"notificationChannels": ["email", "in_app", "dashboard_banner"],
"urgencyLevel": "high",
"includeMigrationLink": true,
"offerSupport": true
},
"exit_routing": {
"customers_notified": "webhook_tester",
"delivery_failed": "retry_notification"
}
}
```
#### Step 6: Test Webhook Integrations
```json
{
"skill": "webhook_tester",
"input": {
"deprecationId": "{{chain.deprecationId}}",
"webhookEndpoints": "{{customer.webhook_urls}}",
"testPayloads": "{{chain.new_version_payloads}}",
"validationRules": "strict",
"retryLogic": "test",
"timeoutSettings": "verify"
},
"exit_routing": {
"tests_passed": "integration_health",
"tests_failed": "customer_support_alert"
}
}
```
#### Step 7: Monitor Integration Health
```json
{
"skill": "integration_health",
"input": {
"deprecationId": "{{chain.deprecationId}}",
"customerId": "{{customer.id}}",
"healthMetrics": [
"api_success_rate",
"response_times",
"error_rates",
"version_adoption",
"webhook_delivery_rate"
],
"monitoringWindow": "continuous",
"alerting": "proactive"
},
"output": "health_dashboard"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/api-lifecycle \
-H "Content-Type: application/json" \
-d '{
"endpoint": "/api/v1/users",
"deprecationReason": "Moving to /api/v2/users with improved schema",
"timelineMonths": 6,
"affectedCustomers": 150
}'
```
### Expected Output
```json
{
"deprecationPlanned": true,
"deprecationId": "dep_001",
"timeline": {
"announcement": "2026-03-01",
"final_notification": "2026-08-01",
"sunset_date": "2026-09-01"
},
"changelogPublished": true,
"migrationDocsUrl": "https://docs.example.com/migrations/v1-to-v2",
"customersNotified": 150,
"webhookTestsPassed": 142,
"webhookTestsFailed": 8,
"migrationProgress": {
"completed": 0,
"in_progress": 0,
"not_started": 150
},
"supportTicketsCreated": 3
}
```
### Monitoring & Metrics
Track these KPIs:
- **Migration Completion Rate:** Target > 95% before sunset
- **Breaking Change Impact:** Target < 5% customer disruption
- **Developer Satisfaction:** Target > 4.5/5 for deprecation process
- **Time to Migrate:** Track average customer migration time
- **Support Burden:** Track support tickets per deprecation
### Expected Outcomes
- **Week 1:** Deprecation announced, migration docs published
- **Month 1:** 30% of customers started migration
- **Month 3:** 70% of customers migrated
- **Month 6:** 95%+ migration completion, smooth sunset
---
## Recipe 22: Security Code Review Automation
**Use Case:** Automate security code reviews with vulnerability detection, variant analysis, and fix recommendations
**Skills:** 6 chained skills
**Time:** Real-time scanning vs manual quarterly audits
**ROI:** 70% faster vulnerability detection, 80% faster remediation, 90% reduction in security debt
### The Chain
```
semgrep-rule-creator → entry-point-analyzer → variant-analysis →
differential-review → fix-review → property-based-testing
```
### How It Works
1. **semgrep-rule-creator** — Creates custom security rules for codebase-specific patterns
2. **entry-point-analyzer** — Identifies code entry points and attack surfaces
3. **variant-analysis** — Finds similar vulnerabilities across the codebase
4. **differential-review** — Reviews code changes for security implications
5. **fix-review** — Validates security fixes and suggests improvements
6. **property-based-testing** — Generates tests to verify security properties
### Setup Instructions
#### Step 1: Install Security Skills
```bash
npx skills-directory install --target all --domain security
```
#### Step 2: Configure Security Rule Creation
```json
{
"skill": "semgrep-rule-creator",
"trigger": "vulnerability_identified",
"input": {
"vulnerabilityType": "{{trigger.vuln_type}}",
"language": "{{repo.primary_language}}",
"codeContext": "{{trigger.code_sample}}",
"severity": "{{trigger.severity}}",
"ruleScope": "project_wide",
"autofix": true,
"testCases": "generate"
},
"exit_routing": {
"rule_created": "entry-point-analyzer",
"needs_expert_input": "security_team_review"
}
}
```
#### Step 3: Analyze Entry Points
```json
{
"skill": "entry-point-analyzer",
"input": {
"repository": "{{repo.path}}",
"securityRules": "{{previous.rules}}",
"entryPointTypes": [
"api_endpoints",
"user_inputs",
"file_uploads",
"authentication",
"database_queries"
],
"attackSurface": "calculate",
"dataFlowTracking": true
},
"exit_routing": {
"entry_points_mapped": "variant-analysis",
"critical_surface_found": "immediate_alert"
}
}
```
#### Step 4: Find Vulnerability Variants
```json
{
"skill": "variant-analysis",
"input": {
"vulnerabilityPattern": "{{trigger.pattern}}",
"entryPoints": "{{previous.entry_points}}",
"codebase": "{{repo.path}}",
"similarityThreshold": 0.75,
"analysisDepth": "deep",
"includeIndirect": true,
"prioritize": "by_exploitability"
},
"exit_routing": {
"variants_found": "differential-review",
"no_variants": "fix-review"
}
}
```
#### Step 5: Review Code Changes
```json
{
"skill": "differential-review",
"input": {
"pullRequest": "{{trigger.pr_number}}",
"changedFiles": "{{pr.files}}",
"securityFocus": [
"authentication_bypass",
"injection_attacks",
"data_exposure",
"access_control",
"cryptography_misuse"
],
"compareAgainst": "main",
"severityFiltering": "high_critical_only"
},
"exit_routing": {
"issues_found": "fix-review",
"clean": "approve_pr",
"critical_issue": "block_merge"
}
}
```
#### Step 6: Review Proposed Fixes
```json
{
"skill": "fix-review",
"input": {
"originalVulnerability": "{{chain.vulnerability}}",
"proposedFix": "{{trigger.fix_code}}",
"reviewCriteria": {
"completeness": true,
"noNewVulnerabilities": true,
"performanceImpact": true,
"compatibilityCheck": true
},
"suggestImprovements": true,
"referenceSecurePatterns": true
},
"exit_routing": {
"fix_approved": "property-based-testing",
"fix_inadequate": "request_revision",
"better_approach": "suggest_alternative"
}
}
```
#### Step 7: Generate Security Tests
```json
{
"skill": "property-based-testing",
"input": {
"vulnerabilityType": "{{chain.vuln_type}}",
"fixedCode": "{{previous.approved_fix}}",
"securityProperties": [
"input_validation",
"output_encoding",
"access_control",
"data_integrity",
"confidentiality"
],
"testFramework": "{{repo.test_framework}}",
"edgeCases": "generate",
"fuzzingEnabled": true
},
"output": "generate_test_suite"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/security-review \
-H "Content-Type: application/json" \
-d '{
"repository": "my-app",
"scanType": "full",
"severityFilter": ["high", "critical"],
"autoFixEnabled": true
}'
```
### Expected Output
```json
{
"status": "success",
"securityScan": {
"vulnerabilitiesFound": 12,
"criticalIssues": 2,
"highIssues": 4,
"mediumIssues": 6,
"variantsIdentified": 8,
"entryPointsAnalyzed": 45,
"attackSurfaceScore": "medium"
},
"remediation": {
"rulesCreated": 5,
"autofixesApplied": 6,
"manualReviewNeeded": 6,
"testsGenerated": 15,
"estimatedFixTime": "4 hours"
},
"recommendations": [
"Add input validation to user profile endpoints",
"Implement rate limiting on authentication routes",
"Update cryptography library to latest version"
]
}
```
### Monitoring & Metrics
Track these KPIs:
- **Vulnerability Detection Rate:** Target 95%+ coverage
- **Mean Time to Detection (MTTD):** Target < 1 day (baseline ~30 days)
- **Mean Time to Resolution (MTTR):** Target < 3 days (baseline ~15 days)
- **False Positive Rate:** Target < 10%
- **Security Debt:** Track trend over time
### Expected Outcomes
- **Week 1:** Automated scanning active, 50+ vulnerabilities detected
- **Month 1:** 70% faster detection, 80% faster remediation
- **Quarter 1:** 90% reduction in security debt, <5 critical issues
- **Quarter 2:** Zero critical vulnerabilities in production
---
## Recipe 23: Superpowers Development Workflow
**Use Case:** Structured development from idea to verified implementation
**Skills:** 4 chained skills
**Time:** Reduces rework; plan before coding, verify before completion
**ROI:** Fewer broken branches, clearer specs, consistent quality
### The Chain
```
superpowers/brainstorming → superpowers/writing-plans →
superpowers/executing-plans → superpowers/verification-before-completion
```
### How It Works
1. **brainstorming** — Explores intent, requirements, and design before implementation
2. **writing-plans** — Produces a concrete implementation plan
3. **executing-plans** — Runs the plan (e.g. code changes)
4. **verification-before-completion** — Validates work before marking done
### Setup Instructions
#### Step 1: Install Superpowers Skills
```bash
npx skills-directory install --target all --domain superpowers
```
#### Step 2: Configure the Chain
```json
{
"skill": "superpowers/brainstorming",
"input": {
"initialIdea": "{{task.description}}",
"projectContext": "{{project}}"
},
"exit_routing": { "success": "superpowers/writing-plans" }
}
```
```json
{
"skill": "superpowers/writing-plans",
"input": {
"design": "{{previous.design}}",
"refinedRequirements": "{{previous.refinedRequirements}}"
},
"exit_routing": { "success": "superpowers/executing-plans" }
}
```
```json
{
"skill": "superpowers/executing-plans",
"input": {
"plan": "{{previous.plan}}",
"context": "{{chain.context}}"
},
"exit_routing": { "success": "superpowers/verification-before-completion" }
}
```
```json
{
"skill": "superpowers/verification-before-completion",
"input": {
"artifacts": "{{previous.output}}",
"successCriteria": "{{chain.design.successCriteria}}"
},
"output": "verified_output"
}
```
### Expected Outcomes
- **Week 1:** Team uses chain for 2–3 features
- **Month 1:** Fewer "done but broken" merges; clearer handoffs
- **Quarter 1:** Habit of plan → execute → verify
---
## Recipe 24: Brand Consistency Engine
**Use Case:** Ensure brand consistency across all content, design, and communication channels with automated brand guideline enforcement and voice validation
**Skills:** 6 chained skills
**Time:** Real-time validation vs manual review cycles
**ROI:** 80% brand compliance, 60% faster asset production, 90% reduction in off-brand content
### The Chain
```
brand_guidelines → skene_voice_guardian → humanization_engine →
copywriting → theme_factory → canvas_design
```
### How It Works
1. **brand_guidelines** — Establishes and enforces brand standards (colors, fonts, tone, messaging)
2. **skene_voice_guardian** — Validates content against brand voice and style guidelines
3. **humanization_engine** — Ensures content feels authentic and human while maintaining brand voice
4. **copywriting** — Generates on-brand copy for various channels
5. **theme_factory** — Creates consistent visual themes across platforms
6. **canvas_design** — Produces on-brand design assets at scale
### Setup Instructions
#### Step 1: Install Marketing Skills
```bash
npx skills-directory install --target all --domain marketing
```
#### Step 2: Configure Brand Guidelines
```json
{
"skill": "brand_guidelines",
"trigger": "content_creation",
"input": {
"brandName": "{{company.name}}",
"brandAssets": {
"primaryColors": ["#FF6B6B", "#4ECDC4", "#45B7D1"],
"secondaryColors": ["#FFA07A", "#98D8C8"],
"fonts": {
"heading": "Montserrat",
"body": "Open Sans"
},
"logoVariants": ["primary", "white", "black", "icon"]
},
"brandVoice": {
"tone": ["professional", "approachable", "innovative"],
"avoidWords": ["cheap", "discount", "revolutionary"],
"preferWords": ["effective", "proven", "innovative"]
},
"messagingPillars": ["Customer-first innovation", "Proven results", "Transparent pricing"]
},
"exit_routing": {
"guidelines_set": "skene_voice_guardian",
"needs_review": "brand_team_approval"
}
}
```
#### Step 3: Validate Brand Voice
```json
{
"skill": "skene_voice_guardian",
"input": {
"content": "{{trigger.content}}",
"brandGuidelines": "{{previous.guidelines}}",
"contentType": "{{trigger.type}}",
"validationRules": {
"toneMatch": true,
"terminologyCheck": true,
"messagingAlignment": true,
"prohibitedWords": true
},
"strictnessLevel": "high"
},
"exit_routing": {
"approved": "humanization_engine",
"violations_found": "revision_required",
"minor_issues": "auto_correct"
}
}
```
#### Step 4: Humanize Content
```json
{
"skill": "humanization_engine",
"input": {
"content": "{{previous.validated_content}}",
"brandVoice": "{{chain.brand_voice}}",
"humanizationGoals": [
"natural_conversational_tone",
"remove_corporate_speak",
"add_personality",
"maintain_professionalism"
],
"preserveBrandTerms": true,
"targetReadability": "grade_8"
},
"exit_routing": {
"humanized": "copywriting",
"needs_refinement": "humanize_again"
}
}
```
#### Step 5: Generate Copy Variants
```json
{
"skill": "copywriting",
"input": {
"baseContent": "{{previous.humanized_content}}",
"brandGuidelines": "{{chain.guidelines}}",
"channels": ["website", "email", "social_media", "ads", "product_descriptions"],
"variants": {
"headlines": 5,
"bodyCopy": 3,
"ctas": 5
},
"optimizeFor": "conversion"
},
"exit_routing": {
"copy_ready": "theme_factory",
"needs_ab_test": "experimentation"
}
}
```
#### Step 6: Create Visual Themes
```json
{
"skill": "theme_factory",
"input": {
"brandGuidelines": "{{chain.guidelines}}",
"contentContext": "{{chain.all_copy}}",
"platforms": ["web", "mobile", "email", "social"],
"themeComponents": {
"colorSchemes": 3,
"typographyPairings": 2,
"layoutTemplates": 5,
"componentStyles": "design_system_compliant"
}
},
"exit_routing": {
"themes_created": "canvas_design",
"needs_designer_input": "design_review"
}
}
```
#### Step 7: Generate Design Assets
```json
{
"skill": "canvas_design",
"input": {
"theme": "{{previous.selected_theme}}",
"copy": "{{chain.copy}}",
"brandAssets": "{{chain.brand_assets}}",
"assetTypes": [
"social_media_posts",
"banner_ads",
"email_headers",
"presentation_slides",
"infographics"
],
"dimensions": "platform_optimized",
"outputFormat": ["png", "svg", "pdf"]
},
"output": "deliver_assets"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/brand-consistency \
-H "Content-Type: application/json" \
-d '{
"contentType": "blog_post",
"topic": "Product Launch Announcement",
"channel": "website",
"designNeeds": ["hero_image", "social_cards"]
}'
```
### Expected Output
```json
{
"status": "success",
"brandCompliance": "100%",
"voiceScore": 95,
"humanizationScore": 88,
"assetsGenerated": {
"copyVariants": {
"headlines": 5,
"bodyCopy": 3,
"ctas": 5
},
"designAssets": {
"hero_image": "launch-hero-v1.png",
"social_cards": ["twitter-card.png", "linkedin-card.png", "facebook-card.png"],
"email_header": "launch-email-header.png"
}
},
"validationsPassed": ["tone_check", "terminology_check", "color_compliance", "font_compliance"],
"productionTime": "45 minutes"
}
```
### Monitoring & Metrics
Track these KPIs:
- **Brand Compliance Rate:** Target > 95% (baseline ~60%)
- **Asset Production Speed:** Target 80% faster than manual
- **Off-Brand Content:** Target < 5% rejection rate
- **Designer Time Saved:** Track hours per month
- **Content Consistency Score:** Target > 90% across channels
### Expected Outcomes
- **Week 1:** Brand guidelines enforced, voice validation active
- **Month 1:** 80% reduction in off-brand content rejections
- **Quarter 1:** 60% faster asset production, 95%+ brand compliance
- **Quarter 2:** $50K+ saved in design/creative time
---
## Recipe 25: Employee Onboarding Automation
**Use Case:** Automate employee onboarding with compensation benchmarking, skill gap analysis, performance tracking, and DEI monitoring
**Skills:** 6 chained skills
**Time:** 2-4 hours automated onboarding vs 2-3 weeks manual process
**ROI:** 70% faster onboarding, 40% cost reduction, 25% better retention
### The Chain
```
people_onboarding_checklist → people_comp_benchmarker → people_skill_gap_analyzer →
people_perf_review_generator → people_pulse_analyzer → people_dei_tracker
```
### How It Works
1. **people_onboarding_checklist** — Automates onboarding tasks, documentation, and system access
2. **people_comp_benchmarker** — Benchmarks compensation against market rates
3. **people_skill_gap_analyzer** — Identifies skill gaps and training needs
4. **people_perf_review_generator** — Generates structured performance reviews
5. **people_pulse_analyzer** — Tracks employee sentiment and engagement
6. **people_dei_tracker** — Monitors diversity, equity, and inclusion metrics
### Setup Instructions
#### Step 1: Install People Ops Skills
```bash
npx skills-directory install --target all --domain people_ops
```
#### Step 2: Configure Onboarding Checklist
```json
{
"skill": "people_onboarding_checklist",
"trigger": "employee_hired",
"input": {
"employeeId": "{{trigger.employeeId}}",
"role": "{{employee.role}}",
"department": "{{employee.department}}",
"startDate": "{{employee.start_date}}",
"checklistItems": [
"equipment_setup",
"system_access",
"benefits_enrollment",
"team_introductions",
"training_modules"
]
},
"exit_routing": {
"checklist_complete": "people_comp_benchmarker",
"items_pending": "reminder_sequence"
}
}
```
#### Step 3: Benchmark Compensation
```json
{
"skill": "people_comp_benchmarker",
"input": {
"employeeId": "{{chain.employeeId}}",
"role": "{{employee.role}}",
"location": "{{employee.location}}",
"experienceLevel": "{{employee.years_experience}}",
"benchmarkSources": ["market_data", "industry_surveys", "peer_companies"],
"includeEquity": true
},
"exit_routing": {
"benchmark_complete": "people_skill_gap_analyzer",
"compensation_alert": "hr_review"
}
}
```
#### Step 4: Analyze Skill Gaps
```json
{
"skill": "people_skill_gap_analyzer",
"input": {
"employeeId": "{{chain.employeeId}}",
"currentSkills": "{{employee.resume_skills}}",
"roleRequirements": "{{role.required_skills}}",
"teamSkills": "{{team.skill_matrix}}",
"analysisDepth": "comprehensive",
"recommendTraining": true
},
"exit_routing": {
"gaps_identified": "people_perf_review_generator",
"no_gaps": "people_pulse_analyzer"
}
}
```
#### Step 5: Generate Performance Framework
```json
{
"skill": "people_perf_review_generator",
"input": {
"employeeId": "{{chain.employeeId}}",
"skillGaps": "{{previous.gaps}}",
"performanceFramework": "OKRs",
"reviewCycle": "quarterly",
"goals": "auto_generate_from_role",
"includeDevelopmentPlan": true
},
"exit_routing": {
"framework_set": "people_pulse_analyzer",
"needs_manager_input": "manager_review"
}
}
```
#### Step 6: Track Employee Pulse
```json
{
"skill": "people_pulse_analyzer",
"trigger": "scheduled",
"frequency": "weekly",
"input": {
"employeeId": "{{chain.employeeId}}",
"pulseQuestions": ["engagement", "satisfaction", "workload", "team_dynamics"],
"anonymity": "partial",
"trendAnalysis": true,
"benchmarkAgainst": "team_avg"
},
"exit_routing": {
"pulse_healthy": "people_dei_tracker",
"concerns_detected": "manager_alert",
"burnout_risk": "hr_intervention"
}
}
```
#### Step 7: Monitor DEI Metrics
```json
{
"skill": "people_dei_tracker",
"input": {
"employeeId": "{{chain.employeeId}}",
"metrics": [
"representation_by_level",
"pay_equity",
"promotion_rates",
"retention_by_demographic"
],
"aggregationLevel": "team",
"privacyPreserving": true,
"reportingFrequency": "quarterly"
},
"output": "aggregate_dei_dashboard"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/employee-onboarding \
-H "Content-Type: application/json" \
-d '{
"employeeId": "emp_789",
"role": "Senior Software Engineer",
"department": "Engineering",
"location": "San Francisco",
"startDate": "2026-03-01"
}'
```
### Expected Output
```json
{
"onboardingComplete": true,
"checklistCompletionRate": 100,
"compensationBenchmark": {
"market_percentile": 65,
"status": "competitive",
"recommendation": "within_range"
},
"skillGapsIdentified": 2,
"trainingRecommended": ["Advanced Kubernetes", "System Design"],
"performanceFrameworkSet": true,
"okrsGenerated": 3,
"pulseScore": 8.5,
"deiMetricsRecorded": true,
"estimatedOnboardingTime": "3 hours"
}
```
### Monitoring & Metrics
Track these KPIs:
- **Onboarding Completion Time:** Target < 1 week (baseline 2-3 weeks)
- **Employee Satisfaction (90-day):** Target > 8/10
- **Retention Rate (1-year):** Target 25% improvement
- **Time to Productivity:** Target 50% reduction
- **DEI Representation:** Track progress toward company goals
### Expected Outcomes
- **Week 1:** 10 employees onboarded through automated system
- **Month 1:** 70% reduction in onboarding administrative time
- **Quarter 1:** 25% improvement in 90-day employee satisfaction scores
- **Quarter 2:** 40% cost reduction in onboarding process
---
## Recipe 26: Data Quality Automation
**Use Case:** Automate data quality monitoring, validation, and anomaly detection across data pipelines
**Skills:** 6 chained skills
**Time:** Real-time data quality monitoring vs weekly manual audits
**ROI:** 80% reduction in data errors, 90% faster issue detection, $100K+ savings/year
### The Chain
```
data_anomaly_alerter → data_event_validator → data_cohort_builder →
data_funnel_optimizer → data_experiment_analyzer → data_dashboard_builder
```
### How It Works
1. **data_anomaly_alerter** — Detects anomalies in data pipelines and metrics
2. **data_event_validator** — Validates event schema, data types, and business rules
3. **data_cohort_builder** — Builds user cohorts for analysis and experimentation
4. **data_funnel_optimizer** — Analyzes and optimizes conversion funnels
5. **data_experiment_analyzer** — Analyzes A/B test results and statistical significance
6. **data_dashboard_builder** — Builds automated dashboards for data monitoring
### Setup Instructions
#### Step 1: Install Data Ops Skills
```bash
npx skills-directory install --target all --domain data_ops
```
#### Step 2: Configure Anomaly Detection
```json
{
"skill": "data_anomaly_alerter",
"trigger": "data_event",
"frequency": "continuous",
"input": {
"dataSource": "{{trigger.source}}",
"metrics": ["volume", "freshness", "completeness", "accuracy"],
"anomalyThreshold": "3_standard_deviations",
"lookbackWindow": "7_days",
"alertChannels": ["slack", "email", "pagerduty"]
},
"exit_routing": {
"anomaly_detected": "data_event_validator",
"normal": "continue_monitoring"
}
}
```
#### Step 3: Validate Event Data
```json
{
"skill": "data_event_validator",
"input": {
"eventType": "{{trigger.event_type}}",
"eventData": "{{trigger.data}}",
"schemaValidation": true,
"businessRules": [
"revenue_must_be_positive",
"timestamps_must_be_sequential",
"user_id_must_exist"
],
"strictMode": false
},
"exit_routing": {
"valid": "data_cohort_builder",
"invalid": "quarantine_and_alert",
"needs_enrichment": "data_enrichment_pipeline"
}
}
```
#### Step 4: Build User Cohorts
```json
{
"skill": "data_cohort_builder",
"input": {
"userId": "{{previous.user_id}}",
"cohortDefinitions": [
{ "name": "weekly_active", "criteria": "weekly_sessions >= 3" },
{ "name": "power_users", "criteria": "monthly_actions >= 100" },
{ "name": "at_risk", "criteria": "days_since_last_activity > 14" }
],
"updateFrequency": "daily",
"historicalWindow": "90_days"
},
"exit_routing": {
"cohorts_built": "data_funnel_optimizer",
"cohort_empty": "adjust_criteria"
}
}
```
#### Step 5: Optimize Conversion Funnels
```json
{
"skill": "data_funnel_optimizer",
"input": {
"funnelId": "{{trigger.funnel_id}}",
"funnelSteps": ["signup", "activation", "first_purchase", "retention"],
"cohort": "{{previous.cohort}}",
"optimizationGoal": "maximize_conversion",
"analysisWindow": "30_days",
"identifyDropoffs": true
},
"exit_routing": {
"optimization_complete": "data_experiment_analyzer",
"needs_experiment": "create_ab_test"
}
}
```
#### Step 6: Analyze Experiments
```json
{
"skill": "data_experiment_analyzer",
"input": {
"experimentId": "{{trigger.experiment_id}}",
"variants": ["control", "variant_a", "variant_b"],
"primaryMetric": "conversion_rate",
"secondaryMetrics": ["time_to_convert", "ltv", "retention"],
"statisticalMethod": "bayesian",
"confidenceLevel": 0.95
},
"exit_routing": {
"winner_found": "data_dashboard_builder",
"inconclusive": "extend_experiment",
"significant_result": "rollout_winner"
}
}
```
#### Step 7: Build Monitoring Dashboard
```json
{
"skill": "data_dashboard_builder",
"input": {
"dashboardType": "data_quality",
"metrics": "{{chain.all_metrics}}",
"visualizations": ["time_series", "funnel", "cohort_retention", "experiment_results"],
"refreshRate": "real_time",
"recipients": ["data_team", "product_team", "executive_team"]
},
"output": "publish_dashboard"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/data-quality \
-H "Content-Type: application/json" \
-d '{
"dataSource": "production_events",
"eventType": "user_action",
"timeWindow": "last_24_hours",
"qualityChecks": ["schema", "volume", "freshness"]
}'
```
### Expected Output
```json
{
"dataQualityScore": 98,
"anomaliesDetected": 0,
"eventsValidated": 1500000,
"invalidEvents": 30000,
"quarantinedEvents": 50,
"cohortsBuilt": 12,
"funnelConversionRate": 23.5,
"experimentsAnalyzed": 3,
"dashboardUrl": "https://dashboard.example.com/data-quality",
"recommendations": [
"Investigate spike in invalid timestamps",
"Extend experiment #47 for 3 more days",
"Optimize funnel step 2 (30% dropoff)"
]
}
```
### Monitoring & Metrics
Track these KPIs:
- **Data Quality Score:** Target > 95%
- **Anomaly Detection Time:** Target < 5 minutes
- **Invalid Event Rate:** Target < 2%
- **Data Pipeline Uptime:** Target > 99.9%
- **Time to Resolution:** Target < 1 hour for critical issues
### Expected Outcomes
- **Week 1:** Data quality monitoring active across all pipelines
- **Month 1:** 80% reduction in data errors reaching production
- **Quarter 1:** 90% faster anomaly detection and resolution
- **Quarter 2:** $100K+ savings from prevented data quality issues
---
## Recipe 27: Compliance Automation Hub
**Use Case:** Automate compliance monitoring, audit preparation, and regulatory reporting across GDPR, SOC 2, and other frameworks
**Skills:** 6 chained skills
**Time:** Real-time monitoring vs quarterly manual audits
**ROI:** 90% faster compliance reporting, 60% risk reduction, 80% audit prep time savings
### The Chain
```
compliance_gdpr_manager → compliance_soc2_tracker → compliance_audit_preparer →
compliance_vendor_risk → compliance_pii_detector → compliance_data_retention
```
### How It Works
1. **compliance_gdpr_manager** — Manages GDPR compliance requirements and data subject requests
2. **compliance_soc2_tracker** — Tracks SOC 2 controls and evidence collection
3. **compliance_audit_preparer** — Prepares audit packages and documentation
4. **compliance_vendor_risk** — Assesses third-party vendor compliance risks
5. **compliance_pii_detector** — Identifies and protects personally identifiable information
6. **compliance_data_retention** — Enforces data retention and deletion policies
### Setup Instructions
#### Step 1: Install Compliance Skills
```bash
npx skills-directory install --target all --domain compliance
```
#### Step 2: Configure GDPR Management
```json
{
"skill": "compliance_gdpr_manager",
"trigger": "data_subject_request",
"input": {
"requestType": "{{trigger.type}}",
"dataSubject": "{{trigger.user_id}}",
"complianceFrameworks": ["GDPR", "CCPA", "LGPD"],
"actions": {
"dataMapping": true,
"consentTracking": true,
"rightToAccess": true,
"rightToErasure": true,
"dataPortability": true
},
"responseDeadline": "30_days",
"automateWherePossible": true
},
"exit_routing": {
"request_fulfilled": "compliance_soc2_tracker",
"manual_review_needed": "legal_team_review",
"deadline_approaching": "escalate"
}
}
```
#### Step 3: Track SOC 2 Controls
```json
{
"skill": "compliance_soc2_tracker",
"input": {
"trustServiceCriteria": ["security", "availability", "confidentiality"],
"controls": [
{
"id": "CC6.1",
"category": "Logical_and_Physical_Access_Controls",
"frequency": "continuous"
},
{
"id": "CC7.2",
"category": "System_Operations",
"frequency": "daily"
}
],
"evidenceCollection": "automated",
"auditReadiness": "calculate",
"gapAnalysis": true
},
"exit_routing": {
"controls_met": "compliance_audit_preparer",
"gaps_identified": "remediation_plan",
"critical_failure": "immediate_escalation"
}
}
```
#### Step 4: Prepare Audit Package
```json
{
"skill": "compliance_audit_preparer",
"input": {
"auditType": "{{trigger.audit_type}}",
"frameworks": "{{chain.frameworks}}",
"evidenceScope": {
"policies": true,
"procedures": true,
"controls": true,
"incidentReports": true,
"accessLogs": true,
"changeManagement": true
},
"timePeriod": "last_12_months",
"format": "auditor_preferred",
"autoGenerate": "executive_summary"
},
"exit_routing": {
"package_ready": "compliance_vendor_risk",
"missing_evidence": "evidence_collection_trigger"
}
}
```
#### Step 5: Assess Vendor Risk
```json
{
"skill": "compliance_vendor_risk",
"input": {
"vendors": "{{company.third_party_vendors}}",
"riskCategories": [
"data_processing",
"security_posture",
"compliance_certifications",
"breach_history",
"geographic_location"
],
"assessmentFrequency": "quarterly",
"riskScoring": "weighted",
"includeSubprocessors": true,
"actionThresholds": {
"high_risk": "immediate_review",
"medium_risk": "quarterly_review",
"low_risk": "annual_review"
}
},
"exit_routing": {
"assessment_complete": "compliance_pii_detector",
"high_risk_vendor": "vendor_audit_trigger"
}
}
```
#### Step 6: Detect PII
```json
{
"skill": "compliance_pii_detector",
"input": {
"scanTargets": ["databases", "file_storage", "logs", "backups", "code_repositories"],
"piiTypes": ["email", "phone", "ssn", "credit_card", "address", "biometric", "health_data"],
"classification": "automatic",
"anonymizationSuggestions": true,
"alertOnUnprotected": true
},
"exit_routing": {
"pii_mapped": "compliance_data_retention",
"unprotected_pii": "encryption_trigger"
}
}
```
#### Step 7: Enforce Data Retention
```json
{
"skill": "compliance_data_retention",
"input": {
"dataCategories": "{{previous.pii_categories}}",
"retentionPolicies": {
"user_data": "7_years",
"logs": "2_years",
"analytics": "3_years",
"marketing": "2_years_or_consent_withdrawn"
},
"deletionMethod": "secure_permanent",
"verificationRequired": true,
"auditTrail": "maintain",
"automatedDeletion": true,
"exceptions": "legal_hold_aware"
},
"output": "retention_report"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/compliance-automation \
-H "Content-Type: application/json" \
-d '{
"complianceCheck": "full_audit_prep",
"frameworks": ["GDPR", "SOC2"],
"reportFormat": "executive_summary"
}'
```
### Expected Output
```json
{
"status": "success",
"complianceStatus": {
"gdpr": {
"compliant": true,
"dataSubjectRequests": 45,
"requestsFulfilled": 45,
"averageResponseTime": "8 days"
},
"soc2": {
"controlsMet": "98%",
"evidenceCollected": 1250,
"gapsIdentified": 2,
"auditReadiness": "high"
}
},
"risks": {
"vendorRisks": 3,
"unprotectedPII": 0,
"retentionViolations": 0
},
"auditPackage": {
"documentsIncluded": 85,
"evidenceItems": 1250,
"coveragePeriod": "12 months",
"readiness": "audit_ready"
}
}
```
### Monitoring & Metrics
Track these KPIs:
- **Compliance Score:** Target > 95% across all frameworks
- **Data Subject Request Response Time:** Target < 10 days (legal max: 30)
- **Vendor Risk Score:** Track average and trend
- **Audit Preparation Time:** Target 80% reduction vs manual
- **Compliance Incidents:** Target zero violations
### Expected Outcomes
- **Week 1:** Compliance monitoring active, gaps identified
- **Month 1:** 90% faster compliance reporting, real-time dashboards
- **Quarter 1:** 98%+ SOC 2 control coverage, audit-ready
- **Quarter 2:** Zero compliance violations, successful audit completion
---
## Recipe 28: E-commerce Revenue Optimization
**Use Case:** Optimize e-commerce pricing, packaging, and monetization strategies to maximize revenue and margins
**Skills:** 6 chained skills
**Time:** Real-time optimization vs quarterly pricing reviews
**ROI:** 30% revenue increase, 20% margin improvement, 25% better conversion rates
### The Chain
```
pricing_strategy → packaging_optimizer → price_experimentation →
upgrade_trigger → consumption_analyzer → invoice_explainer
```
### How It Works
1. **pricing_strategy** — Analyzes market positioning and recommends optimal pricing
2. **packaging_optimizer** — Designs product packaging and tier structures
3. **price_experimentation** — Runs A/B tests on pricing and packaging
4. **upgrade_trigger** — Identifies optimal moments for upsell/cross-sell
5. **consumption_analyzer** — Tracks product usage and consumption patterns
6. **invoice_explainer** — Provides transparent billing explanations to reduce churn
### Setup Instructions
#### Step 1: Install Monetization Skills
```bash
npx skills-directory install --target all --domain monetization
```
#### Step 2: Configure Pricing Strategy
```json
{
"skill": "pricing_strategy",
"trigger": "pricing_review",
"input": {
"currentPricing": "{{company.pricing_tiers}}",
"competitorPricing": "{{market_research.competitors}}",
"customerSegments": [
{
"segment": "SMB",
"willingness_to_pay": "$50-200/month",
"value_drivers": ["ease_of_use", "support"]
},
{
"segment": "Enterprise",
"willingness_to_pay": "$1000-10000/month",
"value_drivers": ["security", "customization"]
}
],
"pricingModels": ["usage_based", "tiered", "hybrid"],
"optimizeFor": "revenue_and_margin"
},
"exit_routing": {
"strategy_defined": "packaging_optimizer",
"needs_more_data": "customer_research"
}
}
```
#### Step 3: Optimize Packaging
```json
{
"skill": "packaging_optimizer",
"input": {
"pricingStrategy": "{{previous.strategy}}",
"features": "{{product.all_features}}",
"currentTiers": "{{company.tiers}}",
"optimizationGoals": {
"clearValueLadder": true,
"reduceDecisionParalysis": true,
"anchorHigherPrice": true,
"encourageMidTier": true
},
"tierCount": "3-4",
"featureGating": "value_based"
},
"exit_routing": {
"packaging_ready": "price_experimentation",
"needs_stakeholder_input": "pricing_committee_review"
}
}
```
#### Step 4: Experiment with Pricing
```json
{
"skill": "price_experimentation",
"input": {
"experimentType": "ab_test",
"variants": [
{
"name": "control",
"pricing": "{{company.current_pricing}}"
},
{
"name": "variant_a",
"pricing": "{{previous.optimized_pricing}}"
},
{
"name": "variant_b",
"pricing": "{{previous.optimized_pricing_aggressive}}"
}
],
"segments": ["new_customers", "upgrade_candidates"],
"metrics": ["conversion_rate", "revenue_per_customer", "customer_lifetime_value", "churn_rate"],
"duration": "30_days",
"significanceLevel": 0.95
},
"exit_routing": {
"winner_identified": "upgrade_trigger",
"inconclusive": "extend_experiment"
}
}
```
#### Step 5: Trigger Upgrades
```json
{
"skill": "upgrade_trigger",
"input": {
"userId": "{{trigger.user_id}}",
"currentTier": "{{user.plan}}",
"usageData": "{{consumption_analyzer.data}}",
"triggerConditions": [
"approaching_limit",
"premium_feature_attempt",
"high_engagement",
"team_growth"
],
"upgradeIncentives": {
"discount": "first_month_20_off",
"trial": "14_day_premium_trial",
"urgency": "limit_approaching"
},
"timing": "optimal_moment"
},
"exit_routing": {
"upgrade_completed": "consumption_analyzer",
"upgrade_declined": "nurture_sequence"
}
}
```
#### Step 6: Analyze Consumption
```json
{
"skill": "consumption_analyzer",
"input": {
"userId": "{{chain.user_id}}",
"pricingModel": "usage_based",
"usageMetrics": ["api_calls", "storage_gb", "compute_hours", "seats", "transactions"],
"billingPeriod": "monthly",
"predictions": {
"nextMonthUsage": true,
"overageRisk": true,
"downgradeLikelihood": true
},
"notifications": "proactive"
},
"exit_routing": {
"normal_usage": "invoice_explainer",
"overage_predicted": "limit_notification",
"usage_dropping": "engagement_campaign"
}
}
```
#### Step 7: Explain Invoices
```json
{
"skill": "invoice_explainer",
"input": {
"invoiceId": "{{billing.invoice_id}}",
"userId": "{{chain.user_id}}",
"usageData": "{{previous.consumption}}",
"explanationFormat": {
"breakdown": true,
"visualCharts": true,
"compareToPrevious": true,
"costDrivers": true,
"optimizationTips": true
},
"deliveryMethod": ["email", "in_app"],
"allowQuestions": true
},
"output": "send_explained_invoice"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/revenue-optimization \
-H "Content-Type: application/json" \
-d '{
"analysisType": "full_pricing_review",
"includeExperiments": true,
"segments": ["smb", "enterprise"]
}'
```
### Expected Output
```json
{
"status": "success",
"pricingOptimization": {
"recommendedStrategy": "hybrid_usage_based",
"recommendedTiers": 3,
"expectedRevenueLift": "30%",
"expectedMarginImprovement": "20%"
},
"experiments": {
"active": 2,
"completed": 5,
"winningVariants": ["variant_b_tier_structure", "variant_a_pricing"]
},
"consumption": {
"averageUsage": "850 API calls/month",
"overageRate": "12%",
"downgradePredictions": 3
},
"invoiceClarity": {
"disputeRate": "2%",
"clarityScore": 92,
"supportTicketsReduced": "60%"
}
}
```
### Monitoring & Metrics
Track these KPIs:
- **Revenue Per Customer:** Track average and trend
- **Gross Margin:** Target > 75% for SaaS
- **Upgrade Conversion Rate:** Target 15-25%
- **Invoice Dispute Rate:** Target < 3%
- **Pricing Experiment Win Rate:** Track learnings
### Expected Outcomes
- **Week 1:** Pricing analysis complete, experiments launched
- **Month 1:** 15% improvement in upgrade conversion rate
- **Quarter 1:** 30% revenue increase per customer, 20% margin improvement
- **Quarter 2:** $500K+ incremental ARR from optimization
---
## Recipe 29: Partnership Ecosystem Automation
**Use Case:** Automate partner channel management, co-selling, and ecosystem revenue tracking
**Skills:** 6 chained skills
**Time:** Real-time partnership management vs manual quarterly reviews
**ROI:** 40% more partner-sourced pipeline, 25% faster deal cycles, 30% ecosystem revenue growth
### The Chain
```
partner_mapping → nearbound_signal → co_sell_trigger →
deal_registration → partner_influenced_revenue → ecosystem_health
```
### How It Works
1. **partner_mapping** — Identifies and maps potential partner relationships
2. **nearbound_signal** — Detects partnership opportunities through shared connections
3. **co_sell_trigger** — Identifies opportunities for co-selling with partners
4. **deal_registration** — Manages partner deal registration and conflict resolution
5. **partner_influenced_revenue** — Attributes revenue to partner contributions
6. **ecosystem_health** — Tracks overall partnership ecosystem health
### Setup Instructions
#### Step 1: Install Ecosystem Skills
```bash
npx skills-directory install --target all --domain ecosystem
```
#### Step 2: Configure Partner Mapping
```json
{
"skill": "partner_mapping",
"trigger": "partner_discovery",
"input": {
"partnerTypes": [
"technology_partners",
"resellers",
"implementation_partners",
"referral_partners",
"integration_partners"
],
"targetIndustries": "{{company.target_industries}}",
"geographies": "{{company.target_regions}}",
"mappingCriteria": {
"customerOverlap": true,
"complementaryProducts": true,
"marketPresence": true,
"culturalFit": true
},
"prioritizationScore": "calculate"
},
"exit_routing": {
"partners_identified": "nearbound_signal",
"needs_vetting": "partner_evaluation"
}
}
```
#### Step 3: Detect Nearbound Signals
```json
{
"skill": "nearbound_signal",
"input": {
"partners": "{{previous.mapped_partners}}",
"accountData": "{{crm.accounts}}",
"signalTypes": [
"shared_customers",
"mutual_connections",
"simultaneous_engagement",
"integration_usage",
"co_marketing_activity"
],
"enrichmentSources": ["linkedin", "crm", "marketing_automation"],
"signalStrength": "score",
"autoAlert": "high_quality_signals"
},
"exit_routing": {
"strong_signals": "co_sell_trigger",
"weak_signals": "nurture_relationship"
}
}
```
#### Step 4: Trigger Co-Selling
```json
{
"skill": "co_sell_trigger",
"input": {
"opportunityId": "{{crm.opportunity_id}}",
"nearbound_data": "{{previous.signals}}",
"partner": "{{previous.recommended_partner}}",
"coSellCriteria": {
"accountFit": true,
"partnerPresence": true,
"dealSize": "> $50K",
"closeProbability": "> 30%"
},
"introduction": "automated_warm_intro",
"sharedContext": "opportunity_details",
"partnerIncentive": "co_sell_credit"
},
"exit_routing": {
"co_sell_initiated": "deal_registration",
"partner_declined": "try_alternative_partner"
}
}
```
#### Step 5: Register Partner Deals
```json
{
"skill": "deal_registration",
"input": {
"dealId": "{{chain.opportunity_id}}",
"partner": "{{chain.partner}}",
"registrationType": "co_sell",
"dealDetails": {
"accountName": "{{opportunity.account}}",
"estimatedValue": "{{opportunity.amount}}",
"expectedCloseDate": "{{opportunity.close_date}}",
"partnerRole": "{{trigger.role}}"
},
"conflictCheck": "automatic",
"approvalWorkflow": "partner_ops",
"partnerCommission": "calculate"
},
"exit_routing": {
"registered": "partner_influenced_revenue",
"conflict_detected": "conflict_resolution"
}
}
```
#### Step 6: Track Partner-Influenced Revenue
```json
{
"skill": "partner_influenced_revenue",
"input": {
"dealId": "{{chain.opportunity_id}}",
"partner": "{{chain.partner}}",
"attributionModel": "multi_touch",
"influenceTypes": [
"deal_registration",
"co_sell",
"referral",
"integration_usage",
"co_marketing"
],
"revenueAllocation": {
"partner_sourced": "100%",
"partner_influenced": "weighted",
"partner_assisted": "partial"
},
"reportingPeriod": "monthly"
},
"exit_routing": {
"attribution_complete": "ecosystem_health",
"dispute": "partner_review"
}
}
```
#### Step 7: Monitor Ecosystem Health
```json
{
"skill": "ecosystem_health",
"input": {
"partners": "{{company.all_partners}}",
"healthMetrics": {
"pipelineGenerated": true,
"revenueInfluenced": true,
"dealVelocity": true,
"partnerEngagement": true,
"nps": true,
"certifications": true
},
"segmentation": "by_tier",
"benchmarking": "against_targets",
"alertThresholds": {
"pipeline_drop": "-20%",
"engagement_drop": "-30%",
"nps_below": 30
}
},
"output": "ecosystem_dashboard"
}
```
### Testing the Chain
```bash
curl -X POST http://localhost:3000/api/chains/partnership-automation \
-H "Content-Type: application/json" \
-d '{
"analysisType": "full_ecosystem_health",
"includeNearbound": true,
"partnerTiers": ["strategic", "premier", "registered"]
}'
```
### Expected Output
```json
{
"status": "success",
"ecosystemMetrics": {
"totalPartners": 45,
"activePartners": 32,
"partnerSourcedPipeline": "$2.5M",
"partnerInfluencedRevenue": "$800K",
"averageDealCycle": "32 days"
},
"nearbound": {
"strongSignals": 12,
"coSellOpportunities": 8,
"potentialValue": "$1.2M"
},
"dealRegistrations": {
"registered": 15,
"approved": 14,
"conflicts": 1,
"averageApprovalTime": "4 hours"
},
"health": {
"overallScore": 87,
"topPerformers": ["Partner A", "Partner B", "Partner C"],
"atRisk": ["Partner X"]
}
}
```
### Monitoring & Metrics
Track these KPIs:
- **Partner-Sourced Pipeline:** Target 30-40% of total pipeline
- **Partner-Influenced Revenue:** Track % of closed/won deals
- **Deal Velocity:** Target 25% faster with partner involvement
- **Partner Engagement Score:** Track monthly activity levels
- **Ecosystem ROI:** Calculate partner program costs vs revenue
### Expected Outcomes
- **Week 1:** Partner mapping complete, nearbound signals active
- **Month 1:** 12+ co-sell opportunities identified and initiated
- **Quarter 1:** 40% increase in partner-sourced pipeline, 25% faster deals
- **Quarter 2:** 30% ecosystem revenue growth, $500K+ partner-influenced ARR
---
## Recipe 30: People Ops Talent Intelligence
**Use Case:** Source candidates, assess skill gaps, and align compensation benchmarks
**Skills:** 3 chained skills (People Ops)
**Time:** Hours instead of days per role
**ROI:** Faster hiring, data-driven comp
### The Chain
```
people_candidate_sourcer → people_skill_gap_analyzer →
people_comp_benchmarker
```
### How It Works
1. **candidate_sourcer** — Surfaces and ranks candidates
2. **skill_gap_analyzer** — Maps gaps for role or team
3. **comp_benchmarker** — Benchmarks compensation for offers and reviews
### Setup
Install domain: `npx skills-directory install --target all --domain people_ops`. Run from ATS or HRIS; use output for role profiles and offer letters.
### Expected Outcomes
- **Week 1:** First pipeline run for one role
- **Month 1:** 40% faster time-to-fill for piloted roles
- **Quarter 1:** Comp benchmarks used for all offers
---
## Recipe 31: Data Ops Experimentation Pipeline
**Use Case:** Cohorts, experiment analysis, and funnel optimization in one flow
**Skills:** 3 chained skills (Data Ops)
**Time:** Same-day experiment readouts and funnel insights
**ROI:** 50% faster experiment cycles, consistent funnel metrics
### The Chain
```
data_cohort_builder → data_experiment_analyzer → data_funnel_optimizer
```
### How It Works
1. **cohort_builder** — Builds user cohorts for analysis and experiments
2. **experiment_analyzer** — Analyzes A/B tests and significance
3. **funnel_optimizer** — Optimizes conversion funnels from results
### Setup
Install domain: `npx skills-directory install --target all --domain data_ops`. Connect to your analytics/warehouse; chain output to dashboards or growth team.
### Expected Outcomes
- **Week 1:** One experiment analyzed end-to-end
- **Month 1:** All experiments read via chain
- **Quarter 1:** Funnel optimization driven by chain output
---
These quick-reference recipes show how to adapt existing skills for industry-specific use cases.
---
### Quick Reference: Healthcare/Life Sciences Workflows
```
[Scientific domain skills] → compliance_pii_detector → compliance_data_retention
```
**ROI:** 40% faster research workflows, 60% compliance improvement
**Note:** Uses 141 scientific domain skills for healthcare/clinical research workflows. Requires medical knowledge bases for full implementation.
---
### Quick Reference: FinTech/Banking Compliance
```
arr_waterfall → burn_rate_monitor → compliance_gdpr_manager → compliance_audit_preparer
```
**ROI:** 90% faster financial reporting, 80% compliance accuracy
**Note:** Combines finops and compliance skills for banking/fintech regulatory requirements.
---
## Recipe 32: Partnership Deal Flow
```
partner_mapping → nearbound_signal → co_sell_trigger →
deal_registration → partner_influenced_revenue
```
**ROI:** 25% more partner-sourced pipeline
---
## Recipe 33: Scientific Research Synthesis Pipeline
**Use Case:** Turn literature review into structured hypotheses and research output
**Skills:** 4 chained skills (scientific context)
**Time:** Hours instead of days for literature-to-hypothesis workflows
**ROI:** 40% faster research synthesis, consistent output structure
### The Chain
```
scientific/literature-review → scientific/hypothesis-generation →
scientific/research-lookup → scientific/scientific-writing
```
### How It Works
1. **literature-review** — Synthesizes existing literature and identifies gaps
2. **hypothesis-generation** — Generates testable hypotheses from the review
3. **research-lookup** — Enriches with current data and citations
4. **scientific-writing** — Produces structured research output (papers, reports)
### Setup Instructions
#### Step 1: Install Scientific (Context) Skills
```bash
npx skills-directory install --target all --domain scientific
```
#### Step 2: Configure Literature Review Entry Point
```json
{
"skill": "scientific/literature-review",
"input": {
"topic": "{{research.topic}}",
"scope": "{{research.scope}}",
"sources": ["pubmed", "openalex", "biorxiv"]
},
"exit_routing": {
"complete": "scientific/hypothesis-generation",
"needs_refinement": "scientific/literature-review"
}
}
```
#### Step 3: Chain to Hypothesis Generation and Beyond
```json
{
"skill": "scientific/hypothesis-generation",
"input": {
"literatureSummary": "{{previous.output}}",
"researchQuestion": "{{research.question}}"
},
"exit_routing": { "hypotheses_ready": "scientific/research-lookup" }
}
```
```json
{
"skill": "scientific/research-lookup",
"input": {
"hypotheses": "{{previous.hypotheses}}",
"context": "{{previous.output}}"
},
"exit_routing": { "enriched": "scientific/scientific-writing" }
}
```
```json
{
"skill": "scientific/scientific-writing",
"input": {
"content": "{{chain.all_previous_outputs}}",
"format": "paper",
"venue": "{{research.venue}}"
},
"output": "research_output"
}
```
### Expected Outcomes
- **Week 1:** Pipeline runs end-to-end on sample topic
- **Month 1:** 40% faster literature-to-hypothesis cycles
- **Quarter 1:** Reusable for multiple research domains
---
## Recipe 34: Skene Growth Strategy Chain
**Use Case:** From codebase and product to a growth manifest and actionable template
**Skills:** 4 chained skills (Skene)
**Time:** Hours instead of workshops to get a first growth strategy
**ROI:** Clear growth roadmap; PLG and GTM alignment
### The Chain
```
skene_analyze_tech_stack → skene_analyze_growth_hubs →
skene_generate_growth_manifest → skene_generate_growth_template
```
### How It Works
1. **analyze_tech_stack** — Detects framework, language, database, auth, deployment
2. **analyze_growth_hubs** — Identifies viral, onboarding, monetization potential
3. **generate_growth_manifest** — Combines tech + growth gaps into a manifest
4. **generate_growth_template** — Produces actionable PLG growth recommendations
### Setup Instructions
#### Step 1: Install Skene Skills
```bash
npx skills-directory install --target all --domain skene
```
#### Step 2: Configure the Chain
```json
{
"skill": "skene_analyze_tech_stack",
"input": {
"repoPath": "{{project.path}}",
"includeDocs": true
},
"exit_routing": { "success": "skene_analyze_growth_hubs" }
}
```
```json
{
"skill": "skene_analyze_growth_hubs",
"input": {
"techStack": "{{previous.output}}",
"productContext": "{{product.overview}}"
},
"exit_routing": { "success": "skene_generate_growth_manifest" }
}
```
```json
{
"skill": "skene_generate_growth_manifest",
"input": {
"techStack": "{{chain.step_1.output}}",
"growthHubs": "{{previous.output}}"
},
"exit_routing": { "success": "skene_generate_growth_template" }
}
```
```json
{
"skill": "skene_generate_growth_template",
"input": {
"manifest": "{{previous.output}}",
"priorities": "{{company.growth_priorities}}"
},
"output": "growth_template"
}
```
### Expected Outcomes
- **Week 1:** First manifest and template from your repo
- **Month 1:** Strategy doc used for PLG/GTM planning
- **Quarter 1:** Updated quarterly with new analyses
---
## Recipe 35: Community Advocacy Pipeline
**Use Case:** Identify champions, find case study candidates, and feed ambassador program
**Skills:** 3 chained skills (Community)
**Time:** Ongoing; replaces manual hunting
**ROI:** 2x more case studies, faster ambassador recruitment
### The Chain
```
community_champion_identifier → community_case_study_finder →
community_ambassador_program
```
### How It Works
1. **champion_identifier** — Finds power users and advocates
2. **case_study_finder** — Identifies strong case study candidates
3. **ambassador_program** — Manages ambassador pipeline and engagement
### Setup
Install domain: `npx skills-directory install --target all --domain community`. Wire product/usage data into step 1; route high-fit champions to case study and ambassador steps.
### Expected Outcomes
- **Week 1:** Champion list and case study shortlist
- **Month 1:** 2+ new case studies in progress
- **Quarter 1:** Ambassador pipeline 2x
---
## Recipe 36: FinOps Standalone Dashboard
**Use Case:** Burn rate, scenarios, and investor-ready metrics without touching sales/CS chains
**Skills:** 3 chained skills (FinOps)
**Time:** Real-time vs manual board prep
**ROI:** 90% faster financial reporting, single source of truth
### The Chain
```
finops_burn_rate_monitor → finops_scenario_planner → finops_investor_metrics
```
### How It Works
1. **burn_rate_monitor** — Tracks cash burn and runway
2. **scenario_planner** — Models scenarios (e.g. growth, cuts)
3. **investor_metrics** — Produces investor-ready KPIs and visuals
### Setup
Install domain: `npx skills-directory install --target all --domain finops`. Configure with your financial data source; chain outputs to your dashboard or board deck.
### Expected Outcomes
- **Week 1:** Dashboard live with burn and scenarios
- **Month 1:** Board prep time cut by 80%+
- **Quarter 1:** Investor updates automated
---
## Tips for Building Your Own Chains
### 1. Start Simple
Begin with 2-3 skills. Prove value. Then expand.
### 2. Map Exit States
Every skill should have clear exit states that route to next skill or endpoint.
### 3. Include Human-in-the-Loop
For high-stakes decisions, add approval gates.
### 4. Monitor Continuously
Track metrics at each step to identify bottlenecks.
### 5. Iterate Based on Data
Use analytics to refine chains over time.
### 6. Document Your Patterns
Share successful chains with your team.
---
## Troubleshooting Common Issues
### Chain Breaks at Step 3
**Problem:** Skill fails or returns unexpected output
**Solution:**
- Check exit state mapping
- Validate input data format
- Add error handling/retry logic
### Performance Slow
**Problem:** Chain takes too long to execute
**Solution:**
- Run independent skills in parallel
- Cache frequently-used data
- Optimize skill configurations
### Results Not Actionable
**Problem:** Chain outputs are too generic
**Solution:**
- Provide more context in inputs
- Use customer/account-specific data
- Add human review step
---
## Next Steps
1. **Pick a recipe** that matches your use case
2. **Install the skills** for that domain
3. **Test with sample data** before going live
4. **Monitor metrics** to track ROI
5. **Iterate and expand** based on results
**Need help?** Check [Quick Wins](./QUICK_WINS.md) for simpler starting points.
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