Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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youtube-text-fetch
by TakazudoFetch transcript/caption text from YouTube videos using youtube-transcript-api. Use when: (1) User wants text from a YouTube video, (2) User provides YouTube URLs and needs transcripts, (3) User says 'fetch youtube text', 'get captions', 'youtube transcript'. Outputs plain text saved to $HOME/cclogs/{slug}/.
l-ccresdoc-build
by TakazudoBuild and install the CCResDoc Tauri app locally. Use when: (1) User says 'build ccresdoc', 'rebuild ccresdoc', 'install ccresdoc', or 'l-ccresdoc-build', (2) User wants a fresh CCResDoc.app after changing Rust, frontend loading page, or doc site code under $HOME/.claude/doc/. Runs cargo clean -p, cargo tauri build, kills running app, replaces /Applications copy, clears quarantine.
dev-create-b4push-script
by TakazudoCreate a before-push validation script (b4push) and project-level b4push skill. Analyzes the project, identifies check steps (quality, build, test, doc site, e2e), generates scripts/run-b4push.sh, adds package.json entry, creates .claude/skills/b4push/skill.md. Use when: (1) User says 'create b4push', 'add b4push', 'before push script', (2) Setting up a new project's CI/validation workflow.
b4push-wisdom
by TakazudoGuide for setting up before-push validation (b4push) and CI checking. Covers analyzing project structure, creating run-b4push.sh, adding package.json entry, creating project-specific b4push skill, setting up GitHub Actions CI. Use when: (1) User says 'set up b4push', 'add CI', 'before push checks', (2) Setting up a new project's validation workflow, (3) User wants CI + local validation.
jlcpcb-component-finder-update-db
by TakazudoDownload or update the JLCPCB electronic components database for the jlcpcb-component-finder skill. Use when: (1) User says 'update jlcpcb db', 'download jlcpcb database', 'refresh parts database', (2) The jlcpcb-component-finder skill reports database not found, (3) User wants to get the latest component data from JLCPCB/LCSC, (4) User says 'update db', 'update parts db'. Downloads ~0.6 GB split-zip (~5 GB installed) from yaqwsx.github.io/jlcparts.
jlcpcb-bom-generate-from-kicad
by TakazudoConvert KiCad exported BOM and position files to JLCPCB PCBA order format. Use when: (1) User has KiCad BOM CSV and .pos files, (2) User needs to prepare files for JLCPCB PCBA ordering, (3) User mentions converting KiCad exports for JLCPCB, (4) User asks about CPL (Component Placement List) format. Handles BOM conversion (Designation→Comment, sorting), CPL conversion (negating Y, adding mm suffix), integrates with jlcpcb-component-finder for LCSC numbers.
jlcpcb-component-finder
by TakazudoSearch the JLCPCB electronic components database (~7M parts) for hardware projects. Use when: (1) Finding components (resistors, capacitors, inductors, ICs, connectors, diodes, transistors, MOSFETs, op-amps, microcontrollers, sensors, LEDs, voltage regulators, audio jacks, etc.), (2) Looking up part numbers, LCSC (C-prefix), or manufacturer part numbers, (3) Finding alternatives/equivalents, (4) Checking stock at JLCPCB/LCSC, (5) Getting specs (package, footprint), (6) Searching SMD or through-hole parts. Keywords: JLCPCB, LCSC, PCB parts, SMT, PCBA, BOM, component sourcing.
codex-2nd
by TakazudoGet a second opinion from OpenAI Codex CLI on a plan or approach. Use when: (1) During planning phase of /x-as-pr or /x-wt-teams to validate the approach, (2) User says 'codex 2nd', 'second opinion', or 'codex opinion', (3) Wanting an alternative perspective before committing to a plan. Sends context and plan to codex, returns feedback. Called automatically by /x-as-pr and /x-wt-teams during planning.
gco-2nd
by TakazudoGet a second opinion from GitHub Copilot CLI on a plan or approach. Use when: (1) Planning phase of /x-as-pr or /x-wt-teams to validate the approach, (2) User says 'gco 2nd', 'copilot 2nd', or 'copilot opinion', (3) Wanting an alternative perspective before committing to a plan.
opus-2nd
by TakazudoSecond opinion from Claude Opus on a plan or approach. Use when: (1) Planning phase of /big-plan needs a higher-quality review than /codex-2nd / /gco-2nd, (2) User says 'opus 2nd' or 'opus opinion', (3) Wanting Anthropic's larger model to critique a plan. Spawns a general-purpose Agent with model: opus that reads the plan file and returns structured feedback. Anthropic quota — not free.
zpaper-articlify
by TakazudoConvert conversation context into a zpaper blog article via the zpaper-writer subagent. ONLY invoke when the user explicitly asks — NEVER proactively propose. Triggers: 'write zpaper article', 'zpaper記事', 'zpaperに書いて', 'articlify for zpaper', or /zpaper-articlify. Gathers context, creates a writing brief, delegates to the writer subagent.
zpaper-apply-voice
by TakazudoApply Takazudo's zpaper blog writing voice and vocabulary rules to text. Use when: (1) User wants to write/rewrite text in Takazudo's zpaper style, (2) User says 'apply voice', 'zpaper voice', 'zpaper文体で', 'zpaper風に書いて', 'ブログ文体を適用', (3) User provides text to transform to zpaper style. Reads writing-style.md and vocabulary-rule.md from the zpaper repo and applies the rules.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.