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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js-project-init
by bdfinstInitialize a new JavaScript project with ES modules, functional style, prettier, eslint, editorconfig, vitest, and gitignore. Use this skill whenever the user wants to start a new JS project, scaffold a Node.js app, create a new package, bootstrap a JavaScript repo, or says things like "init a new project", "set up a JS project", "create a new node app", "start a new frontend project", or "bootstrap a new package". Also trigger when the user asks to add standard JS tooling (linting, formatting, testing) to an empty or near-empty directory.
semgrep-analyze
by bdfinstRun Semgrep static analysis on target files and return structured findings. Use this when the user wants static analysis, SAST scanning, or security scanning — phrases like "run semgrep", "scan for vulnerabilities", "static analysis on this code", or as a pre-review gate when security findings are needed before AI agents run.
add-plugin
by bdfinstInstall a Claude Code plugin and register it in settings.json so the full team can replicate the install. Use this whenever adding a new plugin to the project — it keeps settings.json in sync with what is actually installed.
adr-tools
by bdfinstCreate and manage Architecture Decision Records using the adr-tools CLI (https://github.com/npryce/adr-tools). Use when the user asks to "add an ADR", "record this decision", "create an ADR", "supersede ADR N", "link ADRs", "generate the ADR table of contents", or any request involving the `adr` command. Pairs with the adr-author agent: this skill is the mechanics (commands, files, links); adr-author is the decision framework (when an ADR is warranted) and the prose authoring.
agent-add
by bdfinstCreate a new Claude Code agent file (review or team type) following the official sub-agent schema and token-efficiency budgets. Use when the user wants to add a new review agent, detect a new category of code issue, create a team agent persona, or says things like "add an agent for X", "create a reviewer for Y", "new team agent for Z". Also use when given a URL to a coding standard that should become a review agent.
agent-audit
by bdfinstAudit code-review agents, skills, and hooks for structural compliance. Use this when adding or modifying any agent, skill, or hook file, or for a periodic health check of the toolkit. Trigger phrases: "audit the agents", "check compliance", "validate the skills", "are the agents correct", or any time agent/skill files change.
agent-create
by bdfinstCreate new Claude Code sub-agent files following the official schema and token-efficiency budgets. Handles both review agents (JSON output, read-only tools, ≤ 40-line body) and team agents (prose output, action tools, ≤ 75-line body). Use when the user says "add an agent", "create a reviewer for X", "new team agent for Y", or when /agent-add is invoked. Validates against /agent-audit before writing. Updates the agent registry and CLAUDE.md after success.
agent-readiness
by bdfinstScore how ready the current repository is for AI-assisted development against the Agent-Readiness Scorecard. Use when the user asks "how agent-ready is this repo", "score this repo for agents", "agent readiness", or wants a tiered readiness report.
agent-skill-authoring
by bdfinstConventions, anti-patterns, and meta-patterns for writing skills (and the shared agent/skill philosophy). Use when creating or editing a SKILL.md file, or when reviewing the agent-vs-skill separation. For the procedural workflow that generates a new agent file, use the agent-create skill (invoked by /agent-add).
apply-fixes
by bdfinstApply correction prompts generated by /code-review. Use this whenever the user wants to apply, fix, or action the results of a code review — phrases like "apply the fixes", "fix the issues", "apply corrections", or after /code-review has run and produced a corrections/ directory.
benchmark
by bdfinstCapture runtime performance metrics (Core Web Vitals, resource sizes, load times) for web pages. Compare against baselines and performance budgets. Use when the user says "benchmark", "check performance", "page speed", "web vitals", "performance regression", or "how fast is this page".
browse
by bdfinstLaunch a browser to navigate URLs, take screenshots, click elements, and fill forms. Use for visual verification, e2e testing, and interactive debugging.
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.