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2490 Data Eed398f1
ASecurity**Total Skills:** 119
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- Added October 11, 2026
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[](https://www.skillsdirectory.com/skills/tools-only-2490-data-eed398f1)# Data Skills
**Total Skills:** 119
## Risk Distribution
| Risk Level | Count |
| ----------- | ----- |
| 🔴 Critical | 81 |
| 🟡 High | 11 |
| 🔵 Medium | 20 |
| 🟢 Low | 7 |
---
## Skills
| # | Skill ID | Risk | Job to be Done |
| --- | -------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| 1 | `elg_integration_health` | 🔴 Critical | Monitors the health, performance, and usage of partner integrations to ensure reliability and identify optimization opportunities. |
| 2 | `elg_partner_influenced_revenue` | 🔴 Critical | Tracks and attributes revenue influenced by partner activities, providing comprehensive ecosystem ROI analysis. |
| 3 | `elg_ecosystem_intelligence` | 🔴 Critical | Aggregates and analyzes ecosystem data to provide strategic insights on partner performance, market trends, and competitive dynamics. |
| 4 | `prodops_documentation_health` | 🟡 High | Monitors documentation coverage, accuracy, and freshness to ensure product documentation remains helpful and current. |
| 5 | `prodops_accessibility_auditor` | 🔴 Critical | Audits product accessibility compliance against WCAG standards and tracks remediation progress. |
| 6 | `prodops_experimentation` | 🔵 Medium | Manages A/B tests and feature experiments with statistical rigor, enabling data-driven product decisions. |
| 7 | `prodops_user_interview_synthesizer` | 🔴 Critical | Synthesizes insights from user interviews into actionable product recommendations with thematic analysis. |
| 8 | `prodops_feedback_synthesis` | 🟢 Low | Aggregates and synthesizes customer feedback into actionable product insights. |
| 9 | `prodops_api_deprecation` | 🔴 Critical | Manages API deprecation lifecycle including usage analysis, migration planning, and developer communication. |
| 10 | `prodops_feature_adoption` | 🔵 Medium | Tracks feature adoption metrics and identifies opportunities to drive usage. |
| 11 | `prodops_voc_aggregation` | 🟢 Low | Aggregates Voice of Customer data from all touchpoints into unified insights. |
| 12 | `prodops_dogfooding_tracker` | 🔵 Medium | Tracks internal usage of the product, collecting feedback from employees to improve quality and empathy. |
| 13 | `prodops_prioritization_engine` | 🔴 Critical | Applies prioritization frameworks (RICE, ICE, weighted scoring) to rank features and initiatives objectively. |
| 14 | `prodops_performance_budget` | 🔴 Critical | Monitors web performance against defined budgets, alerting on regressions and tracking Core Web Vitals. |
| 15 | `prodops_impact_analyzer` | 🔵 Medium | Analyzes the impact of shipped features on key metrics and business outcomes with causal inference methods. |
| 16 | `devex_integration_health` | 🔴 Critical | Scores and monitors the health of developer integrations based on usage patterns, error rates, and best practices. |
| 17 | `devex_deprecation_notifier` | 🔴 Critical | Manages deprecation communications, tracks developer migration progress, and ensures smooth feature sunsets. |
| 18 | `devex_sdk_version_monitor` | 🔴 Critical | Monitors SDK usage across developers, identifies outdated versions, and proactively notifies about updates and security patches. |
| 19 | `devex_latency_advisor` | 🔵 Medium | Analyzes API call patterns and provides recommendations to reduce latency and improve response times. |
| 20 | `devex_sandbox_manager` | 🔴 Critical | Provisions and manages isolated sandbox environments for safe API experimentation and testing. |
| 21 | `devex_rate_limit_advisor` | 🔵 Medium | Monitors API usage patterns, predicts rate limit issues, and provides optimization recommendations. |
| 22 | `support_sla_monitor` | 🔴 Critical | Monitors SLA compliance in real-time, predicts breaches, and triggers escalations to ensure service level commitments. |
| 23 | `support_queue_balancer` | 🔴 Critical | Optimizes ticket distribution across agents and queues to balance workload and maintain SLA compliance. |
| 24 | `support_macros_optimizer` | 🔴 Critical | Analyzes macro usage patterns to identify optimization opportunities, suggest new macros, and retire ineffective ones. |
| 25 | `support_csat_analyzer` | 🔵 Medium | Analyzes customer satisfaction survey results to identify trends, drivers, and improvement opportunities. |
| 26 | `support_sentiment_tracker` | 🔴 Critical | Tracks and analyzes sentiment across ticket conversations to identify at-risk interactions and trending issues. |
| 27 | `support_resolution_suggester` | 🔴 Critical | Suggests optimal resolutions for support tickets by analyzing similar past cases and knowledge base content. |
| 28 | `finops_rule_of_40` | 🔵 Medium | Tracks growth + profitability metric against SaaS benchmarks to measure business health |
| 29 | `finops_payback_period` | 🔵 Medium | Calculates CAC payback period to measure go-to-market efficiency and capital requirements |
| 30 | `finops_magic_number` | 🟡 High | Calculates SaaS Magic Number to measure sales and marketing efficiency for investment decisions |
| 31 | `finops_benchmark_comparator` | 🔴 Critical | Compares company metrics against industry benchmarks and peer cohorts for strategic positioning |
| 32 | `finops_gross_margin` | 🔴 Critical | Analyzes gross margin by product, customer segment, and cost component for profitability optimization |
| 33 | `finops_expense_allocator` | 🔴 Critical | Allocates expenses across departments, products, and cost centers for accurate financial reporting |
| 34 | `finops_ndr_tracker` | 🔵 Medium | Tracks Net Dollar Retention to measure revenue expansion efficiency and customer value growth |
| 35 | `compliance_soc2_tracker` | 🔴 Critical | Monitors and manages SOC 2 compliance status across Trust Service Criteria with continuous control monitoring |
| 36 | `cursor_rules/prisma` | 🟢 Low | Enforce best practices for Prisma ORM, ensuring type-safe, performant, and maintainable database interactions in modern TypeScript applications. |
| 37 | `cursor_rules/grafana` | 🟢 Low | This guide defines definitive best practices for developing, structuring, and maintaining Grafana dashboards, alerts, and data sources as code, ensuring readability, performance, and scalability across teams. |
| 38 | `cursor_rules/sqlalchemy` | 🟢 Low | This guide enforces modern SQLAlchemy 2.x best practices for Python applications, ensuring type-safe, performant, and maintainable database interactions. |
| 39 | `cursor_rules/remix` | 🟢 Low | This guide outlines definitive best practices for building robust, performant, and maintainable applications with Remix, emphasizing co-location, type safety, and efficient data handling. |
| 40 | `cursor_rules/drizzle` | 🟢 Low | This guide provides definitive, opinionated best practices for using Drizzle ORM to build robust, type-safe, and performant database applications in TypeScript. |
| 41 | `plg_habit_loop_builder` | 🔴 Critical | Designs and reinforces product usage habits through cue-routine-reward cycles, driving sustainable engagement and retention. |
| 42 | `plg_feature_request_handler` | 🔴 Critical | Captures, categorizes, and responds to feature requests while identifying existing solutions and setting appropriate expectations. |
| 43 | `plg_empty_state_optimizer` | 🔴 Critical | Transforms empty states from dead-ends into engagement opportunities with contextual guidance, sample data, and clear next actions. |
| 44 | `plg_onboarding_flow` | 🔴 Critical | Orchestrates multi-step onboarding flows with personalization based on user type, plan, and goals. |
| 45 | `plg_sandbox_environment` | 🔴 Critical | Provisions and manages isolated sandbox environments with sample data for safe product exploration without commitment. |
| 46 | `plg_trial_extension_evaluator` | 🔴 Critical | Evaluates trial extension requests and proactively identifies users who would benefit from extended trials to maximize conversion. |
| 47 | `plg_viral_loop` | 🔴 Critical | EXECUTION: Triggers and optimizes viral loops for organic user acquisition through referrals, invites, and sharing. Uses lifecycle, analytics, and messaging tools to orchestrate the viral flow. For strategy and design frameworks, see plg_frameworks/viral-loops. |
| 48 | `plg_guided_setup_wizard` | 🔴 Critical | Orchestrates personalized multi-step setup flows that configure the product based on user goals, industry, and preferences. |
| 49 | `plg_contextual_help` | 🔴 Critical | Delivers intelligent, context-aware help content and guidance based on user's current location, action, and history. |
| 50 | `plg_progressive_disclosure` | 🔴 Critical | Intelligently reveals advanced features and complexity only when users are ready, preventing overwhelm while enabling power users. |
| 51 | `plg_aha_moment_detection` | 🔴 Critical | Identifies and triggers aha moments - the specific actions that correlate with long-term retention. |
| 52 | `plg_friction_detector` | 🔴 Critical | Identifies and analyzes user experience friction points through behavioral signals, enabling proactive intervention and UX optimization. |
| 53 | `plg_quick_win_generator` | 🔵 Medium | Identifies and surfaces immediate value opportunities that users can achieve within minutes, building momentum and confidence. |
| 54 | `plg_interactive_tour` | 🔴 Critical | Creates dynamic step-by-step product walkthroughs with contextual guidance that adapts to user behavior and feature familiarity. |
| 55 | `plg_reverse_trial` | 🔴 Critical | Manages reverse trial experiences where users start with full premium access and gracefully transition to free tier, maximizing conversion through loss aversion. |
| 56 | `plg_time_to_value` | 🔴 Critical | Monitors and optimizes time-to-value, identifying blockers and accelerating first value achievement. |
| 57 | `plg_personalized_checklist` | 🔴 Critical | Creates dynamic, personalized onboarding and task checklists based on user goals, role, and behavior patterns. |
| 58 | `data_anomaly_alerter` | 🔴 Critical | Detects anomalies in key metrics using statistical methods and ML, alerting teams to significant deviations. |
| 59 | `data_event_validator` | 🔴 Critical | Validates tracking event schemas for data quality, ensuring events conform to specifications and catching issues before they pollute analytics. |
| 60 | `data_attribution_modeler` | 🟡 High | Builds and compares marketing attribution models to understand channel effectiveness and optimize spend allocation. |
| 61 | `data_etl_monitor` | 🔴 Critical | Monitors ETL/ELT pipeline health, detects failures and delays, and ensures data freshness SLAs are met. |
| 62 | `data_warehouse_optimizer` | 🔴 Critical | Analyzes and optimizes data warehouse queries, identifying slow queries and recommending performance improvements. |
| 63 | `data_funnel_optimizer` | 🔴 Critical | Analyzes conversion funnels, identifies drop-off points, and recommends optimizations to improve conversion rates. |
| 64 | `data_experiment_analyzer` | 🔵 Medium | Analyzes A/B test and experiment results with statistical rigor, determining winners and providing actionable insights. |
| 65 | `people_dei_tracker` | 🔴 Critical | Tracks diversity, equity, and inclusion metrics across the employee lifecycle to measure progress and identify improvement opportunities. |
| 66 | `people_offboarding_manager` | 🔴 Critical | Manages employee departures with compliance-focused checklists, knowledge transfer, and exit interview analysis. |
| 67 | `people_comp_benchmarker` | 🟡 High | Analyzes compensation data against market benchmarks to ensure competitive and equitable pay practices. |
| 68 | `people_pulse_analyzer` | 🔴 Critical | Analyzes employee pulse surveys, engagement data, and sentiment to surface actionable insights and early warning signals. |
| 69 | `anthropic_official/pdf` | 🔴 Critical | Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale. |
| 70 | `cs_product_adoption_guidance` | 🟡 High | Provides personalized product adoption guidance, recommending features and workflows that align with customer goals and use cases. |
| 71 | `community_event_manager` | 🔴 Critical | Plans, promotes, and manages community events including meetups, webinars, and virtual gatherings. |
| 72 | `community_contributor_tracker` | 🟡 High | Tracks and nurtures open source contributors including PRs, issues, and community contributions. |
| 73 | `community_forum_moderator` | 🔴 Critical | Assists with community forum moderation including content review, user management, and maintaining healthy discussions. |
| 74 | `community_swag_manager` | 🔴 Critical | Manages community swag inventory, distribution, and fulfillment for events, rewards, and recognition programs. |
| 75 | `community_content_curator` | 🔵 Medium | Curates, organizes, and surfaces the best community-generated content for maximum visibility and engagement. |
| 76 | `ai_chatbot_orchestrator` | 🔴 Critical | Manages conversational AI flows, context, and handoffs between bot and human agents. |
| 77 | `ai_agent_performance` | 🔴 Critical | Monitors and analyzes AI agent performance, accuracy, and health across all deployed agents. |
| 78 | `ai_summarization_agent` | 🔵 Medium | Generates concise, accurate summaries from long-form content including documents, threads, and reports. |
| 79 | `revops_sales_content_recommender` | 🟡 High | Recommends relevant sales collateral, case studies, and battle cards based on deal context and buyer journey. |
| 80 | `revops_data_enrichment` | 🟡 High | Automatically enriches lead, contact, and account data using multiple data sources for improved targeting and personalization. |
| 81 | `revops_competitive_intel` | 🔴 Critical | Tracks competitive landscape, win/loss patterns against competitors, and provides real-time battle card intelligence. |
| 82 | `revops_win_loss_analyzer` | 🔵 Medium | Analyzes closed deals to extract actionable insights on win/loss patterns and competitive dynamics. |
| 83 | `revops_next_best_action` | 🔴 Critical | Recommends the most impactful next action for each deal based on stage, signals, and historical patterns. |
| 84 | `revops_attribution_tracker` | 🔵 Medium | Tracks and attributes revenue to marketing campaigns, channels, and touchpoints using multi-touch attribution models. |
| 85 | `mon_mrr_movement_tracker` | 🔴 Critical | Tracks MRR changes including new, expansion, contraction, churn, and reactivation |
| 86 | `mon_credits_balance_tracker` | 🔵 Medium | Tracks prepaid credits, consumption, expiration, and balance management |
| 87 | `mon_cohort_ltv_analyzer` | 🔴 Critical | Analyzes lifetime value by cohort to optimize acquisition and retention strategies |
| 88 | `mon_usage_metering` | 🔴 Critical | Tracks and reports usage for consumption-based billing models. |
| 89 | `mon_revenue_recognition` | 🔵 Medium | Assists with ASC 606 compliant revenue recognition and reporting |
| 90 | `mon_price_experimentation` | 🔴 Critical | Designs, executes, and analyzes pricing experiments to optimize revenue |
| 91 | `mon_entitlement_manager` | 🟡 High | Manages feature access, usage limits, and plan entitlements in real-time |
| 92 | `mon_packaging_optimizer` | 🔴 Critical | Optimizes product packaging, bundles, and tier structures for maximum revenue |
| 93 | `mon_tax_compliance` | 🔴 Critical | Manages sales tax, VAT, and GST compliance across jurisdictions |
| 94 | `mon_consumption_analyzer` | 🟡 High | Analyzes usage trends for billing optimization and forecasting |
| 95 | `mon_overage_predictor` | 🔵 Medium | Predicts usage overages and proactively notifies customers before billing surprises |
| 96 | `boyce_growth_team_orchestrator` | 🔴 Critical | Structures and manages cross-functional growth teams for PLG execution. Based on Dave Boyce's FREEMIUM framework (Chapter 5, with Ben Williams): Growth teams are small, cross-functional teams organized by objective, not function. |
| 97 | `boyce_plg_audit` | 🔴 Critical | Comprehensive PLG audit synthesizing all frameworks from Dave Boyce's FREEMIUM book. Evaluates First Impact, Growth Teams, Pricing, Loops, Retention, Multi-GTM, and more. |
| 98 | `boyce_plg_business_case` | 🟡 High | Builds executive-ready PLG investment cases with fit assessment, resource planning, and ROI projections. Based on Dave Boyce's FREEMIUM framework (Chapter 4): PLG is a 1-3 year strategic investment, not a quick tactic. |
| 99 | `boyce_first_impact_optimizer` | 🔴 Critical | Optimizes the #1 critical event that drives compound growth in PLG. Based on Dave Boyce's FREEMIUM framework (Chapter 8): First Impact must be delivered within minutes or hours to prevent user abandonment. |
| 100 | `boyce_enterprise_plg_transition` | 🔴 Critical | Guides established sales-led companies in adding PLG. Based on Dave Boyce's FREEMIUM framework (Chapter 16): The biggest trap is trying to PLG-ify the entire product at once. |
| 101 | `boyce_plg_pricing_architect` | 🔴 Critical | Designs PLG-native pricing with Free→Pro→Team→Enterprise tiers. Based on Dave Boyce's FREEMIUM framework (Chapter 10): Price according to what the customer values, not arbitrary units. |
| 102 | `plgf_retention_analysis` | 🔴 Critical | Retention curves, cohort analysis, churn prediction, engagement scoring, and resurrection strategies |
| 103 | `plgf_feature_gating` | 🔴 Critical | Gate types (hard/soft/usage/time/team), reverse trials (Elena Verna), and competitive free tier analysis |
| 104 | `plgf_product_analytics` | 🔴 Critical | Complete product analytics framework including event taxonomy design, tool setup (Mixpanel/Amplitude/PostHog), funnel analysis, cohort analysis, and behavioral analytics. |
| 105 | `plg_frameworks/plg-metrics` | 🔴 Critical | When the user wants to define PLG metrics, build a growth dashboard, or set KPI targets -- including activation rate, free-to-paid conversion, NRR, or North Star metric. Also use when the user says "PLG dashboard," "growth KPIs," "metric definitions," or "PLG benchmarks." For activation-specific metrics, see activation-metrics. For analytics setup, see product-analytics. |
| 106 | `plgf_signup_flow_cro` | 🔴 Critical | Field-by-field optimization, social auth analysis, single vs multi-step flows, and mobile optimization |
| 107 | `plgf_product_led_sales` | 🔴 Critical | Design PQL/PQA scoring models, sales handoff workflows, segment-based approaches, and CRM integration patterns |
| 108 | `plgf_in_product_messaging` | 🔴 Critical | Comprehensive framework for in-product messaging including tooltips, banners, modals, targeting rules, frequency management, and accessibility compliance. |
| 109 | `plgf_usage_based_pricing` | 🔴 Critical | Comprehensive framework for implementing consumption-based pricing models including credit systems, overage handling, AI product pricing, and billing infrastructure. |
| 110 | `scientific/networkx` | 🔴 Critical | Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships. |
| 111 | `scientific/exploratory-data-analysis` | 🔵 Medium | Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats. |
| 112 | `scientific/fred-economic-data` | 🔴 Critical | Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators. |
| 113 | `scientific/medchem` | 🔴 Critical | Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering. |
| 114 | `scientific/histolab` | 🔴 Critical | Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml. |
| 115 | `scientific/simpy` | 🔴 Critical | Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time. |
| 116 | `scientific/hypothesis-generation` | 🔴 Critical | Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic. |
| 117 | `scientific/opentargets-database` | 🔴 Critical | Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification. |
| 118 | `scientific/scikit-learn` | 🔴 Critical | Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices. |
| 119 | `scientific/labarchive-integration` | 🔴 Critical | Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows. |
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