**Total Skills:** 61
Scanned 5/31/2026
Install via CLI
Skills Directoryopenskills install tools-only/X-Skills# Customer Success Skills
**Total Skills:** 61
## Risk Distribution
| Risk Level | Count |
| ----------- | ----- |
| 🔴 Review | 45 |
| 🟡 Elevated | 11 |
| 🔵 Standard | 5 |
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## Skills
| # | Skill ID | Risk | Job to be Done |
| --- | --------------------------------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | `elg_partner_tier_manager` | 🔴 Review | Manages partner tier classifications, evaluates tier progression, and tracks tier-based benefits and requirements. |
| 2 | `prodops_beta_program_manager` | 🔴 Review | Manages beta programs including participant selection, feature rollout, feedback collection, and graduation criteria. |
| 3 | `prodops_localization_manager` | 🔵 Standard | Manages product localization including translation coverage, quality monitoring, and locale-specific feature rollouts. |
| 4 | `support_kb_gap_finder` | 🟡 Elevated | Identifies gaps in knowledge base coverage by analyzing support tickets and failed search queries. |
| 5 | `support_proactive_outreach` | 🔴 Review | Identifies opportunities for proactive customer outreach based on usage patterns, known issues, and risk signals. |
| 6 | `support_escalation_predictor` | 🔴 Review | Predicts ticket escalation probability using ML models and historical patterns to enable proactive intervention. |
| 7 | `vcf_value_metrics` | 🟡 Elevated | Builds quantifiable Value Creation Metrics by comparing customer 'before and after' states. Creates the data foundation for ROI calculations and stakeholder-specific value propositions. |
| 8 | `plg_activation` | 🔴 Review | EXECUTION: Guides new users from signup to first value moment, reducing time-to-value and driving activation. Uses lifecycle and messaging tools to orchestrate the activation flow. For strategy and measurement frameworks, see plg_frameworks/activation-metrics. |
| 9 | `plg_network_effect_amplifier` | 🔴 Review | Identifies and accelerates network effects by encouraging collaborative behaviors, invitations, and cross-user interactions that increase product value. |
| 10 | `data_cohort_builder` | 🟡 Elevated | Creates and manages user cohorts based on behavioral, demographic, and custom criteria for targeted analysis and campaigns. |
| 11 | `data_dashboard_builder` | 🔴 Review | Creates and configures analytics dashboards based on business requirements, automatically selecting appropriate visualizations and metrics. |
| 12 | `cs_health_scoring` | 🔴 Review | Calculates and monitors customer health scores to predict retention and identify at-risk accounts. |
| 13 | `cs_customer_maturity_model` | 🔴 Review | Assesses customer maturity across multiple dimensions to guide engagement strategies and identify growth opportunities. |
| 14 | `cs_multi_product_adoption` | 🔵 Standard | Tracks and optimizes adoption across multiple products or modules, identifying cross-sell opportunities and adoption gaps. |
| 15 | `cs_sentiment_analyzer` | 🟡 Elevated | Analyzes customer sentiment across all touchpoints to identify satisfaction trends, detect early warning signs, and guide proactive engagement. |
| 16 | `cs_adoption_score` | 🔴 Review | Calculates comprehensive product adoption scores across user engagement, feature utilization, and workflow integration dimensions. |
| 17 | `cs_time_to_impact` | 🟡 Elevated | Measures and optimizes the time from customer onboarding to measurable business impact and value realization. |
| 18 | `cs_contract_intelligence` | 🔴 Review | Analyzes contract terms, tracks obligations, and provides insights for renewal and expansion conversations. |
| 19 | `cs_advocacy_identifier` | 🟡 Elevated | Identifies potential customer advocates and reference candidates based on satisfaction, engagement, and success signals. |
| 20 | `cs_onboarding_health` | 🔴 Review | Monitors customer onboarding progress, identifies blockers, and ensures successful time-to-value during the critical first 90 days. |
| 21 | `cs_expansion_playbook` | 🔴 Review | Identifies expansion opportunities and executes upsell/cross-sell playbooks. |
| 22 | `cs_executive_alignment` | 🔵 Standard | Tracks executive sponsor engagement and alignment, ensuring strategic partnership at the highest levels of customer organizations. |
| 23 | `cs_quarterly_business_review` | 🔴 Review | Generates comprehensive Quarterly Business Review presentations with value metrics, adoption insights, and strategic recommendations. |
| 24 | `cs_playbook_selector` | 🔴 Review | Intelligently selects and recommends the optimal CS playbook based on customer situation, signals, and historical effectiveness. |
| 25 | `customer_success/feedback_collection` | 🟡 Elevated | Collects structured user feedback through a conversational flow, stores responses, and transitions back to IDLE state upon completion. |
| 26 | `cs_churn_prediction` | 🔴 Review | Predicts churn risk using behavioral signals and triggers proactive retention interventions. |
| 27 | `cs_red_flag_detector` | 🔴 Review | Proactively detects early warning signs of customer issues, churn risk, or dissatisfaction before they escalate. |
| 28 | `cs_stakeholder_mapper` | 🔴 Review | Maps and tracks stakeholder relationships within customer organizations, identifying champions, detractors, and relationship gaps. |
| 29 | `cs_risk_mitigation_playbook` | 🔴 Review | Identifies at-risk accounts and executes structured intervention playbooks to prevent churn and restore customer health. |
| 30 | `cs_escalation_manager` | 🔴 Review | Manages customer escalations through structured workflows, ensuring timely resolution and stakeholder communication. |
| 31 | `cs_csql_generator` | 🔴 Review | Identifies and qualifies expansion and upsell opportunities from the customer success perspective, generating CS-Qualified Leads (CSQLs). |
| 32 | `cs_outcome_tracker` | 🟡 Elevated | Tracks and reports on customer business outcomes, demonstrating ROI and value delivered through product usage. |
| 33 | `cs_nps_followup` | 🔴 Review | Automates NPS survey follow-up with personalized responses based on score and feedback. |
| 34 | `cs_value_realization` | 🔵 Standard | Tracks and documents customer value realization against defined success criteria (VLAs). |
| 35 | `cs_success_plan_generator` | 🟡 Elevated | Creates comprehensive, personalized success plans that map customer goals to product capabilities and define clear milestones for value realization. |
| 36 | `cs_customer_journey_orchestrator` | 🔴 Review | Orchestrates personalized customer journeys across lifecycle stages, triggering contextual touchpoints and interventions. |
| 37 | `cs_renewal_orchestration` | 🔴 Review | Orchestrates the renewal process from early warning to close, maximizing GRR and NRR. |
| 38 | `cs_vla_tracker` | 🔴 Review | Defines and tracks VLA outcomes aligned with customer business objectives, ensuring measurable value delivery throughout the customer lifecycle. |
| 39 | `cs_product_gap_reporter` | 🔴 Review | Aggregates and prioritizes customer feature requests and product gaps, providing structured feedback to product teams. |
| 40 | `community_user_group_manager` | 🔴 Review | Manages regional and interest-based user groups, supporting local community leaders and coordinating activities. |
| 41 | `community_ambassador_program` | 🟡 Elevated | Manages the community ambassador program including recruitment, onboarding, activities, and rewards. |
| 42 | `community_champion_identifier` | 🔴 Review | Finds and nurtures community champions based on engagement signals, activity patterns, and influence metrics. |
| 43 | `ai_document_parser` | 🔴 Review | Extracts structured data from unstructured documents including contracts, invoices, and forms. |
| 44 | `ai_intent_classifier` | 🟡 Elevated | Classifies user/customer intent in real-time for intelligent routing and personalized responses. |
| 45 | `mon_invoice_explainer` | 🔵 Standard | Generates clear, detailed explanations of invoice line items and billing changes |
| 46 | `plgf_expansion_revenue` | 🔴 Review | Comprehensive framework for driving Net Revenue Retention (NRR) through expansion signals, in-product upgrade triggers, and account health scoring. |
| 47 | `plg_frameworks/plg-ideas` | 🔴 Review | When the user wants PLG ideas, tactics, or inspiration -- a mega-reference of 100+ product-led growth strategies organized by category. Also use when the user says "growth ideas," "PLG playbook," "growth tactics," "what should we try," or "growth brainstorm." For PLG strategy, see plg-strategy. For experimentation, see growth-experimentation. |
| 48 | `plgf_user_segmentation` | 🔴 Review | Comprehensive user segmentation framework including behavioral cohorts, scoring models, RFM analysis, persona development, and personalization playbooks. |
| 49 | `plgf_mental_models` | 🔴 Review | Access and apply 42 mental models for Product-Led Growth with challenge-to-model lookup matrix |
| 50 | `boyce_usage_retention_optimizer` | 🔴 Review | Optimizes DAU/WAU/MAU retention through habit formation and cohort analysis. Based on Dave Boyce's FREEMIUM framework (Chapter 11): Usage retention is more important than dollar retention. |
| 51 | `boyce_ai_gtm_automator` | 🔴 Review | Designs the human-machine GTM stack for AI-augmented go-to-market. Based on Dave Boyce's FREEMIUM framework (Chapter 19): AI will blend with PLG to accelerate GTM de-laboring. |
| 52 | `plg_frameworks/plg-strategy` | 🔴 Review | When the user wants to assess PLG readiness, design a product-led growth strategy, choose between freemium and free trial, evaluate PLG maturity, or plan a hybrid PLG + sales model. Also use when the user says "should we do PLG," "PLG vs sales-led," "growth motions," "PLG audit," or "go-to-market strategy." For specific mental models, see plg-mental-models. For growth loop design, see growth-loops. |
| 53 | `plgf_product_onboarding` | 🔴 Review | Onboarding architecture design including interactive tours, checklists, empty states, and progressive disclosure patterns |
| 54 | `plgf_trial_optimization` | 🔴 Review | Trial types comparison, length optimization, email sequences, and conversion benchmarks |
| 56 | `plg_frameworks/plg-mental-models` | 🔴 Review | When the user needs mental models or frameworks for PLG decisions -- including product-channel fit, time-to-value, network effects, habit loops, or pricing psychology. Also use when the user asks "what framework should I use," "how should I think about this," or references a specific model like "adjacent user theory" or "bowling alley framework." For comprehensive PLG strategy, see plg-strategy. For growth loops, see growth-loops. |
| 57 | `plgf_metrics` | 🔴 Review | Complete PLG metrics stack including North Star framework, leading indicators, cohort analysis, benchmarks by stage, and dashboard design patterns. |
| 58 | `plgf_feature_adoption` | 🔴 Review | Adoption lifecycle analysis, discovery mechanisms, stickiness analysis, and deprecation communication |
| 59 | `plg_frameworks/growth-experimentation` | 🔴 Review | When the user wants to design, prioritize, or analyze growth experiments -- including A/B tests, hypothesis frameworks, ICE/RICE scoring, or growth sprints. Also use when the user says "A/B test," "experiment design," "growth sprint," "experiment prioritization," or "statistical significance." For analytics setup, see product-analytics. For growth modeling, see growth-modeling. |
| 60 | `scientific/matchms` | 🔴 Review | Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms. |
| 61 | `scientific/pyopenms` | 🔴 Review | Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms. |
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