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Found 2,052 Skills
Run Confused and GuardDog to detect dependency confusion and typosquatting risks. Checks if internal package names exist on public registries and identifies malicious packages.
Senior SaaS CFO / Financial Analyst (15+ years) specialized in financial modeling, projections, and exit strategy for bootstrapped and VC-backed SaaS companies. Activate when user needs: (1) Revenue projections (1-5 years), (2) Exit valuation and multiples, (3) Unit economics analysis (CAC, LTV, payback), (4) Scenario modeling (conservative/base/optimistic), (5) Fundraising narratives with financial backing, (6) M&A due diligence financials, (7) SaaS metrics benchmarking, (8) Cohort analysis and churn modeling. Triggers: "proyecciones", "projections", "exit", "valuation", "ARR", "MRR", "multiples", "revenue forecast", "financial model", "exit strategy", "CAC", "LTV", "unit economics", "churn", "fundraising", "M&A", "acquisition", "5 year plan".
After an agentic task completes, perform a retrospective analysis across 6 dimensions (goal alignment, efficiency, decision quality, error handling, communication, reusability). Score performance, identify inefficiency patterns, evaluate skill usage, and produce actionable improvement recommendations. Triggers on "how did it go", "retrospective", "review performance", "what could be better", or after any long agentic task completes.
Use only when the user explicitly requests brainstorming, evaluating architecture choices, or comparing options where no single concern dominates
Evaluates RAG (Retrieval-Augmented Generation) pipeline quality across retrieval and generation stages. Measures precision, recall, MRR for retrieval; groundedness, completeness, and hallucination rate for generation. Diagnoses failure root causes and recommends chunk, retrieval, and prompt improvements. Triggers on: "audit RAG", "RAG quality", "evaluate retrieval", "hallucination detection", "retrieval precision", "why is RAG failing", "RAG diagnosis", "retrieval quality", "RAG evaluation", "chunk quality", "RAG pipeline review", "grounding check". Use this skill when diagnosing or evaluating a RAG pipeline's quality.
MUST READ before running any ADK evaluation. ADK evaluation methodology — eval metrics, evalset schema, LLM-as-judge, tool trajectory scoring, and common failure causes. Use when evaluating agent quality, running adk eval, or debugging eval results. Do NOT use for API code patterns (use adk-cheatsheet), deployment (use adk-deploy-guide), or project scaffolding (use adk-scaffold).
Define the design rules (Skill Laws) that all Skills must follow, including core principles such as AI-first, human-centric, and ready-to-use. When to use: When users create a new Skill, optimize an existing Skill, ask about Skill design specifications, or need to evaluate Skill quality.
Expert skill for generating GitHub Copilot skills from ING-internal documentation repositories. Use this skill when asked to create a skill from any ING documentation-as-code repo, generate a knowledge base skill for an ING framework, convert ING tool documentation into a Copilot skill, or turn any docs/ folder into an expert skill file. Also trigger when the user mentions "skill from docs", "generate skill", "create skill from repo", or references ING-internal frameworks like Baker, Merak, Kingsroad, or similar. Includes evaluation framework, grading agents, and benchmark tools for testing generated skills.
Systematic design quality evaluation. Hierarchy, type, color, space, craft, system. Use when evaluating whether a design is ready to ship, running quality audits, or setting quality standards.
Implement feature flags using the Vercel Flags SDK with server-side evaluation, environment-based toggles, and Vercel Toolbar integration.
Technical solution evaluation and code review in the style of Linus Torvalds. Only use this when the user explicitly requests a Linus-style review or explicitly asks for a rigorous evaluation of code changes/technical solutions (e.g., "review changes/code", "evaluate if the solution is appropriate", "check submission standards", "linus-tech-review").
Use when designing visual interfaces, data visualizations, educational content, or presentations and need to ensure they align with how humans naturally perceive, process, and remember information. Invoke when user mentions cognitive load, visual hierarchy, dashboard design, form design, e-learning, infographics, or wants to improve clarity and reduce user confusion. Also applies when evaluating existing designs for cognitive alignment or choosing between design alternatives.