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Found 1,747 Skills
GoPlus AgentGuard — AI agent security guard. Automatically blocks dangerous commands, prevents data leaks, and protects secrets. Use when reviewing third-party code, auditing skills, checking for vulnerabilities, evaluating action safety, or viewing security logs.
Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with multi-agent evaluation
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).
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
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.
Investment Analysis: Generate an in-depth investment analysis report. We do not conduct traditional investment analysis—the core judgment is whether the project is an "Order-Creating Machine". Activate this when the user says "investment report", "investment analysis", "analyze this project", "write an investment report", "investment report", "invest analysis", or provides entrepreneur conversation records for investment evaluation. Also activate when the user pastes or references meeting notes, pitch decks, or founder interviews and requests analysis.
Attach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
Complete reference for the Galileo AI platform Python SDK for evaluating, observing, and protecting GenAI applications. Use when building Python applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.
Help users develop product taste and intuition. Use when someone wants to improve their product judgment, struggles to evaluate design quality, needs to make decisions without complete data, or wants to build better product instincts.
Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
Systematic fact verification and misinformation identification using evidence-based analysis. Use when: verifying claims, checking facts, identifying misinformation, evaluating source credibility, or when user asks to "fact check", "verify", "is this true", or mentions claims that need validation.
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").