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Found 13,199 Skills
A building experience: create, test, validate, refine, and publish extraction workflows based on existing or new Nimble agents. For users who want to invest in a durable, reusable workflow for a specific domain — not get data immediately. Trigger phrases: "set up extraction for X site", "I need to extract from this site regularly", "build an agent for", "create a reusable scraper", "generate a Nimble agent", "refine my agent", "add a field to my agent", or when the user wants to run extraction at scale. For getting data immediately, use nimble-web-expert instead.
Interact with Channel Talk workspaces using API credentials - send messages, read chats, manage groups and bots
Audit AI agent skills for security vulnerabilities. Use when scanning installed skills against the OWASP Agentic Skills Top 10, checking skills before running them, gating CI/CD on skill safety, or generating audit reports (text, JSON, SARIF, HTML) for stakeholders.
macOS screen capture, window recording, GIF conversion, and agent evidence bundles from the terminal. Built on ScreenCaptureKit for window-level targeting ffmpeg cannot do. Use when the user wants a screenshot of a specific window or app, a screen recording, a GIF conversion, a before/after diff, an evidence bundle for a PR, OCR text from a window, a terminal VHS recording, a Remotion render, or wants to watch a UI for changes. Requires macOS Screen Recording permission on first run.
ローカル改修した `.agents/skills/<skill-name>/` を upstream リポジトリ (Fandhe-AI/agent-cli-skills 等) へ PR として投稿する。`skills-lock.json` の `source` を読み、`Fandhe-AI/` 以外への push は安全弁で中止。clone → 反映 → セキュリティチェック → ブランチ作成 → push → `gh pr create` を実行。マージ後は sync-skills-lock で hash 更新。「スキルを upstream に貢献」「外部リポジトリに PR」などで使用。
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature.
Designs and refactors software codebases to be AI-friendly by aligning the filesystem with domain/feature boundaries, creating deep (greybox) modules with small public interfaces, enforcing import boundaries, and tightening tests/feedback loops. Use when the user asks to "make the codebase AI-ready", "reduce coupling", "introduce deep modules", "create module boundaries", "restructure folders by feature", "define service interfaces", or "plan a refactor + tests so AI agents can work safely".
Scans the project and configures checks and reviews for Agent Validator for requests such as "set up validator", "configure checks and reviews", or "initialize validator for this repo".
Generate a /goal mega prompt for Claude Code or Codex CLI by interviewing the user about their task. Use when the user wants to define a long-horizon autonomous goal — migration, refactor, feature build, optimization loop, test fixing, research project, learning system, or any task where the agent should run end-to-end without hand-holding. Trigger on: "help me write a goal", "I want Claude to keep working until...", "run this autonomously", "set a /goal", or any request that implies sustained agentic execution toward a non-trivial outcome. The skill conducts a structured interview (one question at a time) to extract outcome, context, success criteria, constraints, and quality bar — then outputs a filled-in mega prompt ready to paste into Claude Code or Codex.
Automatically persists every plan produced by the agent as a structured Markdown file under .agentic/plans/ with a datestamped slug and updates the plans index. Prevents PLAN.md from being accidentally committed to the repository. Invoked automatically at the end of any planning session — no user prompt required. Triggered whenever an agent completes a plan, proposes a multi-step approach, or produces an architectural decision.
Track, optimize, and control token consumption across multi-agent systems. Covers budget allocation, real-time monitoring, cost attribution, per-agent limits, and proactive cost optimization for production LLM deployments.
Production-grade engineering skills for AI coding agents - lifecycle commands, workflow automation, and best practices for software development.