The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 79 404 files from 1 741 authors, of which 61 763 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Read or configure the vault filing methodology and suggest destinations for planned knowledge creation under Generic, LYT, PARA, or Zettelkasten. Use for wiki mode, methodology mode, what is my vault mode, set vault mode, switch to PARA, use LYT, Zettelkasten setup, change mode, configure mode, or methodology routing. This skill does not save content or migrate notes.
Answer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions here.
Build and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta, remote egress requires explicit consent, and unavailable reranking falls back deterministically.
Initialize, adopt, and route work for a separate Obsidian knowledge vault through the portable claude-obsidian core. Use for vault setup, scaffolding, workspace selection, cross-project configuration, or choosing the correct wiki sub-skill. Triggers: /wiki, set up wiki, scaffold vault, create knowledge base, adopt this vault, Obsidian vault, second brain setup, persistent wiki.
Set up and use portless for named local dev server URLs (e.g. https://myapp.localhost instead of http://localhost:3000). Use when integrating portless into a project, configuring dev server names, setting up the local proxy, working with .localhost domains, or troubleshooting port/proxy issues.
Configure OAuth providers (Google, Apple, Microsoft, Facebook, GitHub, etc.) to work with portless local dev URLs. Use when setting up OAuth redirect URIs, fixing "redirect_uri_mismatch" or "invalid redirect" errors, configuring sign-in providers for local development, or when a provider rejects .localhost subdomains. Triggers include "OAuth not working with portless", "redirect URI mismatch", "Google/Apple/Microsoft sign-in fails locally", "configure OAuth for local dev", or any task involving OAuth callback URLs with portless domains.
把用户提供的素材(网页 URL / PDF / DOCX / Markdown / 纯文本 / 截图 / 粘贴材料)编辑、设计成一篇美丽的、可离线打开和分享的**单文件 HTML 网页文章**。基于 reacticle 组件协议:不手写裸 HTML/CSS,而用语义组件 + 受主题约束的 Raw 自由层;按 source→规划→双确认→生成→终审→修复的小型 harness 流程推进,默认 100% 信息保留的长文。触发场景:把 URL/PDF/DOCX/文章做成网页文章 / 长文 / briefing / 解释文 / 视觉文章 / 教程 / 审阅复盘 / 方案分析,'render this as a beautiful web article / 把这篇做成网页文章 / 生成一篇可分享的 HTML 长文 / reacticle 文章'。只生成文章,不生成后台、表单、dashboard、产品原型或通用 Web App。
Build or redesign polished browser-rendered visual artifacts with HTML/CSS/JavaScript/React: pages, dashboards, prototypes, slide decks, animations, UI mockups, and data visualizations. Use for visual front-end creation, design-system exploration, design critique, or explicit browser acceptance / QA of a web artifact. Not for back-end, CLI, non-visual coding, source-to-longform article conversion, or narration-driven click-through video presentations.
把一篇文章或口播稿,做成"看起来像视频"的点击驱动 16:9 网页演示,可选合成口播音频。流程:原始文章 → **一次产出**口播稿 + outline 开发计划 → 用户**一次对齐** 5 件事(稿子 / outline / 主题 / 素材 / 开发模式)→ 网页开发(逐章 / 顺序 / 并行)→ 可选音频合成(provider-agnostic:内置 MiniMax mmx-cli + OpenAI TTS,可换 ElevenLabs / edge-tts / Azure / 自带 TTS)。**outline 只规划节奏与信息密度,不规划动画** —— 动画由章节开发时按 PRINCIPLES + ANTI-AI 法则即时设计。每次点击推进口播稿的一个节拍,每一步独占整屏,进度条平时隐藏只在悬浮时出现。适用场景:用网页做视频(动态 PPT 但不像 PPT)、把口播稿 / 文章变成可交互的解说、为 B 站 / YouTube / 视频号录屏教程、做有电影感的产品 / talk demo。本 Skill 沉淀的是设计方法论 + 协作流程 —— 不绑定任何特定样式 / 字体 / 颜色 —— 因此能复用到任意主题与美学。
面向 GPT Image 2 的图像生成 / 编辑技能。可在 3 种环境下使用:(A) Garden 本地模式,通过 OpenAI 兼容接口直接出图并落盘;(B) Host-Native 模式,把本 Skill 当作提示词工程指引,把渲染好的 prompt 交给宿主 Agent 自带的图像工具出图;(C) Advisor 模式,宿主无任何图像工具时退化为高质量 prompt 顾问。涵盖 18 大类、80+ 个结构化模板,覆盖海报 / UI / 产品 / 信息图 / 学术图 / 技术架构图 / 漫画 / 头像 / 流程板 / 电影分镜 / IP 周边 / 编辑工作流等场景。
面向本地知识库目录的检索和问答助手。核心流程:(1)分层索引导航 (2)遇到PDF/Excel时必须先读取references学习处理方法 (3)处理文件后再检索。按文件类型组合使用 grep、Read、pdfplumber、pandas 进行渐进式检索,避免整文件加载。用户问题涉及"从知识库目录回答问题/检索信息/查资料"时使用。
Scan an ADK recipe directory and generate a manifest.yaml for it based on the schema at .github/schemas/manifest-schema.json. Use when the user wants to create or generate a manifest.yaml for a recipe under core/, contrib/, or skills/.
Install and authenticate, on demand, the CLIs the sandbox does not prebake — Node/npm, `gws` (Google Workspace), `gcloud`, `agents-cli` (call remote A2A/ADK agents), and `mcp-cli` (use MCP-server tools). Use this whenever one of those tools is needed but missing (a `node`/`npm`/`gws`/`gcloud`/`agents-cli`/`mcp-cli` command returns "command not found"), or before starting any task that requires one — Google Workspace work (Drive, Gmail, Sheets, Calendar, Chat), GCP via `gcloud`, calling another agent deployed remotely over HTTP (Cloud Run or Vertex Agent Runtime), or using tools exposed by an MCP server. Setup only (install + config + headless auth); each tool's own usage lives in its own skill(s).
> Aligns a Python recipe's pyproject.toml with the repo's standards enforced by .github/workflows/python-validate-recipe.yml, plus one critical [build-system] presence check. Scope is pyproject.toml only — standalone ruff.toml / .ruff.toml files (also forbidden in recipes) are caught by the --dry-run that reports what needs alignment, and an apply mode that rewrites pyproject.toml (and optionally manifest.yaml) using comment-preserving TOML/YAML editors. Use when the user wants to "align the recipe's pyproject.toml", "fix pyproject to match the repo standard", "check what needs changing in a recipe's pyproject", or clean up a recipe before submitting a PR.
> Generates a lightweight `tests/test_runnability.py` for a Python recipe. The test just imports the recipe's agent module and asserts that `root_agent is not None` (and `app is not None` if the module defines one). The skill parses agent.py with `ast` to figure out which import-time side effects need mocking (`vertexai.init`, `google.auth.default`) and which env vars need setting (`GOOGLE_CLOUD_PROJECT`, `INTEGRATION_TEST`), and only emits the boilerplate the recipe actually needs. Runs in dry-run (report + preview) and apply (write to disk) modes. Use when the user wants to "add a runnability test", "generate test_runnability.py", "create a smoke test for the recipe", or fix the missing-required-file failure from `python-validate-recipe.yml`.
Helps users discover and install NEW agent skills from external sources (skills.sh, the Skills CLI). Use only when the user wants capabilities they don't already have — "find a skill for X", "is there a skill that can...", "how do I do X" for a task not covered by an installed skill. Do NOT use this to list skills you ALREADY have — those are in your `<available_skills>` block; answer from there directly.
> This skill should be used when the user wants to "create a new Python ADK sample", "scaffold a new Python sample recipe", "generate a new Python sample in contrib", "add a new Python sample to the adk-samples repository", or "create a Python adk sample". It utilizes an automated script to copy template files and resolve basic placeholders.
> Scans a Python recipe to find every place an environment variable is accessed — including `os.environ.setdefault("V", "d")`, whose "d" would otherwise be a hidden default a user editing .env.example has no way to discover — then ensures all variables are declared in `.env.example`, that `load_dotenv()` is bootstrapped in the package `__init__.py`, and that `python-dotenv>=1.0.0` is listed in `pyproject.toml`. When a new entry is added to `.env.example`, the extracted default from source is and provenance comment); values that look like stubs (`"my-project-id"`, `"changeme"`, `"<...>"`) are downgraded to the TODO placeholder but the source string is preserved in the marker comment. Also detects hardcoded model-name string literals (e.g. `"gemini-3.5-flash"` in `agent.py`) and rewrites them to an `os.getenv(...)` call. The variable name is derived from the assignment target when it names a model (`DEFAULT_EMBEDDING_MODEL` → `EMBEDDING_MODEL`), else `MODEL_NAME` (single model) or `MODEL_NAME_GENERATED_1` / `MODEL_NAME_GENERATED_2`, … (multiple models). Normally no fallback default is written into the Python source; the one exception is when no `load_dotenv()` bootstrap could be installed (no package `__init__.py`), where the original literal is kept as the fallback so the lookup cannot evaluate to `None` at runtime. The model string is written as the value in `.env.example` with a comment prompting a rename. When re-run against a recipe whose `.env.example` already has entries, the writer classifies each entry (skill-authored vs. user-authored, TODO vs. real value) and only rewrites lines it can prove it authored; user-authored lines are always preserved. A stale TODO (either inline comment) is upgraded in place when source can supply a real default. (1) USER-EDIT SAFETY. Any `.env.example` line the skill cannot prove it authored is USER_OWNED and is never modified. The rewriter fails closed on any structural ambiguity (quoted values, backslash continuation, duplicate declarations) — a stale TODO left in place is cheap; a clobbered user edit is not. (2) ADDITIVE-ONLY FOR PYTHON FILES. The skill never writes new `os.environ.setdefault(...)` bootstrap lines into any Python file. Pre-existing `os.environ.setdefault(...)` or `os.getenv("VAR", "default")` calls that the recipe author wrote by hand are LEFT UNTOUCHED — the skill's only writes to Python files are relative imports that would otherwise trip Ruff; (c) hardcoded model-literal replacement. Use when the user wants to "extract env vars", "update .env.example", "add load_dotenv", "surface setdefault defaults", "upgrade stale TODOs in .env.example", "replace hardcoded model names", or "fix environment variables" in a Python recipe.
Inspect and edit the per-workspace tool-policy overlay (.lha/policies.jsonl) that gates destructive or sensitive tool calls.
How to read and write Google Drive, Docs, Sheets, Gmail, Calendar, Chat, Tasks, Slides, Keep, Forms, Apps Script, and Meet via the `gws` CLI (a community Google Workspace CLI, not an official Google product). `gws` wraps the Workspace REST APIs (pagination, retries, JSON parsing) and the agent shells out via `terminal`. Note that `gws` does NOT ship an OAuth client — an OAuth client or service account must be configured before interactive login will work (unless a pre-injected token is present). Use whenever the user asks for anything involving a Google Workspace surface.
Use when the user wants a recurring task done automatically on a schedule (daily/weekly/cron) that DOES WORK unattended — pull data and summarize, open a PR, post a digest — rather than just a time-based reminder ping, OR when the user wants to test/dry-run/"run now" a routine before scheduling it. Explains how to author a routine, test it once on demand, and schedule it to run in an isolated sandbox with only declared credentials, via the routine tool.
Use when you hit a problem with Horizon *itself* worth telling the maintainers — a tool that keeps erroring, a guardrail that misfired on a benign action, a capability/tool you needed but don't have, or an instruction that was confusing or self-contradictory. Files high-signal feedback via the report_to_maintainers tool, which always asks the user before sending.
Blog post writing skill with structure templates and style guidelines. Guides the agent through writing well-structured, engaging technical blog posts with proper formatting, section flow, and reader engagement techniques.
Content research and SEO writing methodology. Guides the agent through topic research, keyword identification, competitive analysis, and writing SEO-optimized content that ranks well and provides genuine value to readers.
> End-to-end orchestration to prepare or update a Python recipe under core/python/, contrib/python/, or skills/<vertical>/<solution>/ so it passes every check in .github/workflows/python-validate-recipe.yml. Runs eight phases in environment-variable extraction, pyproject.toml alignment, ruff format+check, per-recipe `uv lock`, runnability-test generation, compile-and-run verification of the generated test file, and a final pass through the repo's own `validate manifest` / `validate structure` validators. Assumes the user has already done the manual prep (deactivated any venv, `git pull` and `uv sync` from the repo root, placed the recipe at its target path, renamed if needed). Delegates to the existing sub-skills (generate-manifest, extract-python-environment-variables, align-recipe-pyproject, generate-python-runnability-test) so the master never duplicates their logic. Pauses at fixed decision points (description mismatch, existing test regeneration) AND is free to interrupt for clarification any time a phase's output looks ambiguous, unexpected, or would benefit from a human judgment call — this is an interactive skill by design. Use when the user wants to "prepare a recipe", "update a recipe end to end", "run all the checks and fixes", "make this recipe PR-ready", or invokes it by name.
Performs a strict evaluation of a video asset using Google's official 'ABCD' framework (Attract, Brand, Connect, Direct) based on transcript and metadata.
Saves time by autonomously reading transcripts, synthesizing arguments, and generating direct jump-links to key moments.
Extracts the strongest arguments from heated YouTube comment threads, identifying key battlegrounds and community consensus.
Provides a high-signal briefing on events in a specific location and timeframe, backed by primary video sources and transcripts.
Deconstructs high-performing or viral videos to extract actionable creative insights from metadata and transcript.
Equips sellers with macro industry trends by analyzing trending data and specific analyst/competitor channels.
Identifies and ranks Key Opinion Leaders (KOLs) based on engagement metrics, active rate, and sentiment rather than just views.
Performs high-density targeted extraction across 10+ videos to map semantic landscapes, consensus, and controversies.
Extracts specific local activity recommendations and sentiment from travel vlogs into a shareable HTML BD report.
Extracts the true audience mood and key feedback by analyzing comment sentiment and keyword frequency.
Provides an executive dashboard comparing Creator vs Audience verdicts for a recent client product launch.
Transforms raw metrics and analysis into visual charts and published, shareable HTML reports using Google Cloud Storage.
Launch and automate VS Code Insiders with the Copilot Chat extension using agent-browser via Chrome DevTools Protocol. Use when you need to interact with the VS Code UI, automate the chat panel, test the extension UI, or take screenshots. Triggers include 'automate VS Code', 'interact with chat', 'test the UI', 'take a screenshot', 'launch with debugging'.
Investigate unexpected chat agent behavior by analyzing direct debug logs in JSONL files. Use when users ask why something happened, why a request was slow, why tools or subagents were used or skipped, or why instructions/skills/agents did not load.
**WORKFLOW SKILL** — Create, update, review, fix, or debug VS Code agent customization files (.instructions.md, .prompt.md, .agent.md, SKILL.md, copilot-instructions.md, AGENTS.md). USE FOR: saving coding preferences; troubleshooting why instructions/skills/agents are ignored or not invoked; configuring applyTo patterns; defining tool restrictions; creating custom agent modes or specialized workflows; packaging domain knowledge; fixing YAML frontmatter syntax. DO NOT USE FOR: general coding questions (use default agent); runtime debugging or error diagnosis; MCP server configuration (use MCP docs directly); VS Code extension development. INVOKES: file system tools (read/write customization files), ask-questions tool (interview user for requirements), subagents for codebase exploration. FOR SINGLE OPERATIONS: For quick YAML frontmatter fixes or creating a single file from a known pattern, edit the file directly — no skill needed.
Control headless Chrome via Cloudflare Browser Rendering CDP WebSocket. Use for screenshots, page navigation, scraping, and video capture when browser automation is needed in a Cloudflare Workers environment. Requires CDP_SECRET env var and cdpUrl configured in browser.profiles.
Generate a pull request description for the FAST repository using the provided template.
Generate a feature request issue for the FAST repository using the provided template.
Add documentation for contributors and developers.
Use this guide when working on TypeScript changes in the FAST monorepo — authoring Web Components, writing templates and styles, working with the observable/reactive system, and testing.
Generate a bug report issue for the FAST repository using the provided template.
Use this skill when contributing changes to the FAST monorepo — creating pull requests, generating change files, writing PR descriptions, and keeping documentation up to date.
Use this guide when working on Rust changes in the FAST monorepo.
Review PyTorch tutorials pull requests for content quality, code correctness, build compatibility, and style. Use when reviewing PRs, when asked to review code changes, or when the user mentions "review PR", "code review", or "check this PR".
Instructions for capturing UI state, comparing with mocks, and interacting with an Android device using MCP tools backed by ADB.
Architecture design and phased implementation planning for Bitwarden Android. Use when planning implementation, designing architecture, creating file inventories, or breaking features into phases. Triggered by "plan implementation", "architecture design", "implementation plan", "break this into phases", "what files do I need", "design the architecture".
This skill should be used when writing or reviewing tests for Android code in Bitwarden. Triggered by "BaseViewModelTest", "BitwardenComposeTest", "BaseServiceTest", "stateEventFlow", "bufferedMutableSharedFlow", "FakeDispatcherManager", "expectNoEvents", "assertCoroutineThrows", "createMockCipher", "createMockSend", "asSuccess", "Why is my Bitwarden test failing?", or testing questions about ViewModels, repositories, Compose screens, or data sources in Bitwarden.
This skill should be used when implementing Android code in Bitwarden. Covers critical patterns, gotchas, and anti-patterns unique to this codebase. Triggered by "How do I implement a ViewModel?", "Create a new screen", "Add navigation", "Write a repository", "BaseViewModel pattern", "State-Action-Event", "type-safe navigation", "@Serializable route", "SavedStateHandle persistence", "process death recovery", "handleAction", "sendAction", "Hilt module", "Repository pattern", "implementing a screen", "adding a data source", "handling navigation", "encrypted storage", "security patterns", "Clock injection", "DataState", or any questions about implementing features, screens, ViewModels, data sources, or navigation in the Bitwarden Android app.
Requirements gap analysis and structured specification for Bitwarden Android. Use when refining requirements, analyzing specs, identifying gaps, or producing structured specifications from tickets or descriptions. Triggered by "refine requirements", "gap analysis", "spec review", "requirements analysis", "what's missing from this spec", "analyze this ticket".
> EU AI Act per-system inventory — track each AI system's role (provider, deployer, importer, distributor, authorized representative, product manufacturer) and risk tier (prohibited, high-risk, limited, minimal, GPAI, GPAI+systemic). Role and tier are assessed per system, not per company. Use when the user says "ai inventory", "add an ai system", "what systems do we have", "classify this ai system", "eu ai act register", or "ai system registry".
> Run the cold-start interview — learns your AI governance practice and writes `~/.claude/plugins/config/claude-for-legal/ai-governance-legal/CLAUDE.md` from your AI policy, a reference impact assessment, and key vendor AI agreements. Use when the practice profile is missing or contains `[PLACEHOLDER]` markers, or when user says "set up ai governance plugin", "onboard me", "configure ai governance".
> Keep the AI policy current with practice — weekly sweep of saved AIAs, triage results, and vendor reviews to find policy drift, or direct query for a proposed new AI practice. Use when user says "policy sweep", "does our AI policy cover this", "we want to start doing X — does the policy need updating", "run the policy monitor", or on a recurring schedule.
> Classify a proposed AI use case against your registry — approved, conditional, or not approved — and produce required conditions and next steps. Flags cross-plugin handoffs to privacy or product counsel. Use when user says "triage this use case", "can we use AI for X", "is this approved", "what do we need to do to use AI for X".
> Run an AI impact assessment — structured intake, risk analysis, regulatory classification per regime in scope, policy consistency diff, and recommendation with conditions. Uses the house-style structure learned from the seed impact assessment in `~/.claude/plugins/config/claude-for-legal/ai-governance-legal/CLAUDE.md`. Use when user says "impact assessment for", "assess this AI use case", "run an AIA", "generate an AIA", "we need to document this AI system", "AI risk assessment for X", or follows a conditional triage result.
> Guided customization of your AI governance practice profile — change one thing without re-running the whole cold-start interview. Adjust risk posture, escalation contacts, use-case registry entries, vendor AI positions, AI policy commitments, impact-assessment house style, or matter workspace paths. Use when the user says "change my [thing]", "update my profile", "edit my config", "tune my playbook", or "customize".
Answers built from the skills we actually parsed.