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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leanspec-pr-lifecycle
by codervisorManage a lean-spec PR after it's been pushed — spec-issue linking, CI triage, review-comment discipline, merge-conflict recovery on open PRs, webhook subscription, and CHANGELOG follow-through on merge. Triggers include "CI is failing", "check is red", "link this issue", "Closes vs Part of", "respond to review", "subscribe to PR", "triage PR", "the PR is ready", "PR has conflicts", "branch has conflicts with main", "merge conflict on the PR", or when a github-webhook-activity event arrives on a lean-spec PR. Paired with `leanspec-dev-process` (overall loop), `issue-spec` (spec creation), and `leanspec-pre-push` (which owns the pre-push conflict walkthrough).
leanspec-development
by codervisorDevelopment workflows, commands, publishing, CI/CD, changelog management, and contribution guidelines for LeanSpec. Use when contributing code, fixing bugs, setting up dev environment, running tests or linting, working with the monorepo structure, looking up build/dev/test/publish/format/lint commands, preparing releases, publishing to npm, bumping versions, syncing package versions, testing dev builds, troubleshooting npm distribution, updating changelogs, triggering CI/CD workflows, monitoring build status, debugging failed runs, managing artifacts, checking CI before releases, or researching AI agent runners. Triggers include any development, scripting, publishing, CI/CD, changelog, or runner research task in this project.
leanspec-dev-process
by codervisorThe end-to-end spec-issue-driven dev loop for lean-spec — spec → branch → implement → PR → merge → closure. Use when asked "how do I start work", "what's the process", "SDD loop", "spec-driven development", "how do we ship a change on lean-spec", "from scratch what do I do", or when you're about to begin a non-trivial change on `codervisor/leanspec` and haven't yet decided how to split spec/PR. Delegates to `issue-spec` (spec writing), `leanspec-pre-push` (pre-push checks), `leanspec-pr-lifecycle` (post-push), and `leanspec-development` (commands, CI, publishing, i18n).
leanspec
by codervisorThe spec-coding methodology for AI-assisted development. Use when planning features, creating/refining/implementing/verifying specs, or organising a project. Works with whatever spec backend your team already uses — local markdown, GitHub Issues, Azure DevOps, Jira — by delegating platform-specific details to a LeanSpec adapter.
watch-ci
by codervisorWatch GitHub Actions CI status for the current commit until completion. Use after pushing changes to monitor build results.
leanspec-pre-push
by codervisorRun before pushing code to the lean-spec repo to catch what reviewers and CI will catch later, and confirm the branch has a linked spec issue in a valid state. Reproduces the merge-preview environment, walks through this repo's common merge-conflict patterns, and runs the project's typecheck / clippy / test gates. Triggers include "before push", "ready to push", "pre-push check", "push readiness", "prep for PR", "resolve merge conflict", "merge conflict", "branch has conflicts", "sync with main", or proactively before any git push on a lean-spec branch.
leanspec-sdd
by codervisorSpec-Driven Development methodology for AI-assisted development. Use when working in a LeanSpec project.
clawden-development
by codervisorGuidance for implementing and reviewing changes in ClawDen. Use this skill whenever modifying ClawDen Rust, TypeScript, dashboard, SDK, CLI, config, tests, or docs, especially for refactors, bug fixes, command behavior changes, or cross-crate work. Prefer this skill by default for repository development tasks unless a more specific ClawDen skill applies.
runtime-research
by codervisorResearch upstream claw runtimes using DeepWiki MCP tools to gather accurate metadata, track breaking changes, and align ClawDen's adapters and descriptors with upstream reality. Use when: (1) Adding a new runtime adapter or descriptor and need to gather upstream metadata (channels, config format, ports, capabilities, language, install method), (2) Checking whether an existing runtime's metadata is still accurate, (3) Investigating a specific upstream runtime's architecture, config options, or channel support for any ClawDen integration work, (4) Auditing adapter/descriptor alignment with upstream repos, (5) Answering questions about a claw runtime's features, breaking changes, or migration paths, (6) Working with any claw runtime integration — even if the user doesn't explicitly ask for "research", any runtime-related task benefits from checking upstream first. Requires: mcp_deepwiki MCP tools.
rust-node-bootstrap
by codervisorScaffold a new Rust+Node.js hybrid monorepo with pnpm workspaces, Cargo workspace, CI workflows, and publish infrastructure. Use when: (1) starting a new project from scratch, (2) adding missing infrastructure to an existing repo, (3) figuring out the canonical project structure for a Rust+Node.js tool. Triggers on "bootstrap", "scaffold", "new project", "set up project", or "init" in a Rust+Node.js context.
rust-node-ci
by codervisorGitHub Actions CI/CD for Rust+Node.js hybrid repos. Covers workflow structure, installable composite actions, artifact flow, caching, and dev versioning. Use when: (1) setting up or fixing GitHub Actions workflows, (2) adding CI for a Rust+Node.js project, (3) working with composite actions (setup-workspace, rust-cross-build, compute-version, wait-npm-propagation), (4) debugging CI failures, (5) setting up the cross-platform build matrix. Triggers on "CI", "workflow", "GitHub Actions", "cross-build", "artifact", or work in .github/workflows/.
rust-npm-publish
by codervisorPublish Rust binaries to npm using the optionalDependencies platform package pattern. Covers the full publish pipeline, version sync, workspace:* protocol, and platform package architecture. Use when: (1) publishing Rust binaries to npm, (2) setting up the platform package pattern (main + per-OS packages), (3) debugging publish failures, (4) managing version sync across pnpm + Cargo workspaces, (5) working with workspace:* protocol. Triggers on "publish", "platform packages", "optionalDependencies", "bin.js", "version sync", "workspace protocol", "npm tag", or "prepare-publish".
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.