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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cascading-fleet
by SocketDevPropagate a wheelhouse template change to every fleet repo (or a registry-pin chain to every dependent repo). Packages the canonical fleet-repo list, the FLEET_SYNC=1 sentinel pattern, the worktree-per-repo loop, push-direct + PR-fallback, and worktree-cleanup that survives mid-loop crashes. Use when a wheelhouse template SHA needs to land in every fleet repo, when a registry pin chain needs propagation, or when batching multiple template SHAs into one cascade wave.
updating-checksums
by SocketDevSyncs SHA-256 checksums from GitHub releases to bundle-tools.json. Triggers when user mentions "update checksums", "sync checksums", or after releasing new tool versions.
researching-recency
by SocketDevResearch what the developer community is actually saying and shipping about a tool, library, language, framework, or maintainer over the last 30 days. Fans out across GitHub (issues/PRs/releases), Hacker News, programming subreddits, Lobsters, dev.to, Bluesky, and the web; ranks by real engagement (stars, points, upvotes, reactions) rather than SEO; and synthesizes a cited brief. Use before adopting a dependency, choosing between tools, reading up on a maintainer before a meeting, scoping a feature against what users actually hit, or whenever you need the recent ground truth a stale README or training cutoff won't give you.
fleet-tidying-rolldown-bundles
by SocketDevKeeps rolldown-bundled fleet repos lean — reports (and with --fix, runs `pnpm dedupe` for) collapsible lockfile transitives, checks that Socket-published packages route through the `catalog:` overrides, and flags any `external/` re-export shim that has grown into a fat re-vendored tree. Conservative and no-prompt: the only mutation is a lockfile-only `pnpm dedupe`; anything that would change the published bundle is reported for a human. Use for periodic dependency hygiene on bundle repos, or before a release.
triaging-findings
by SocketDevTriage a batch of raw security findings. Verify each is real, collapse duplicates, re-rank by derived exploitability, and tag with an owner. Takes a directory or file of scanner output (Socket CLI, Trivy, OpenGrep, TruffleHog, scanning-vulns VULN-FINDINGS.json, or any JSON/markdown report) and writes TRIAGE.json + TRIAGE.md sorted by what actually needs engineering attention. Use when asked to "triage findings", "validate scanner output", "prioritize vulns", or "review the security backlog". Runs interactively by default; pass --auto to skip the interview.
tidying-rolldown-bundles
by SocketDevKeeps rolldown-bundled fleet repos lean — reports (and with --fix, runs `pnpm dedupe` for) collapsible lockfile transitives, checks that Socket-published packages route through the `catalog:` overrides, and flags any `external/` re-export shim that has grown into a fat re-vendored tree. Conservative and no-prompt: the only mutation is a lockfile-only `pnpm dedupe`; anything that would change the published bundle is reported for a human. Use for periodic dependency hygiene on bundle repos, or before a release.
release
by SocketDevOrchestrates a release by chaining quality-scan and security-scan as gates, generating a changelog, bumping the version, and offering to publish. Use when preparing a release, cutting a new version, or when `/release-changelog` is invoked.
squashing-history
by SocketDevSquashes all commits on the repo's default branch (main, falling back to master) to a single conventional-commit "chore: initial commit" with backup branch, integrity verification, and user confirmation before force push. Use when cleaning history or preparing for fresh start.
updating-workflows
by SocketDevExecutes the GitHub Actions SHA pin cascade when a socket-registry action or workflow changes. Lands one layer at a time (direct push to main by default; PR only when the repo's branch protection requires it), waits for each layer's merge SHA, and propagates the final SHA to all consuming repos. Use when workflow files or actions change, or when asked to update workflows or cascade SHAs.
updating-upstream
by SocketDevBumps git submodules declared in `.gitmodules` to their latest stable upstream tag, for submodules NOT managed by an lockstep `version-pin` row. Reads the `# <name>-<version>` comment above each submodule as the current pin, finds the latest stable tag (excluding pre-releases), checks out, updates the comment, commits atomically. Invoked by the `updating` umbrella skill; can also be invoked standalone.
tidying-rolldown-bundles
by SocketDevKeeps rolldown-bundled fleet repos lean — reports (and with --fix, runs `pnpm dedupe` for) collapsible lockfile transitives, checks that Socket-published packages route through the `catalog:` overrides, and flags any `external/` re-export shim that has grown into a fat re-vendored tree. Conservative and no-prompt: the only mutation is a lockfile-only `pnpm dedupe`; anything that would change the published bundle is reported for a human. Use for periodic dependency hygiene on bundle repos, or before a release.
reordering-release-bump
by SocketDevMoves an existing `chore: bump version to X.Y.Z` commit to the tip of the default branch when work landed on top of it, then retags vX.Y.Z onto the moved commit and force-pushes — with a timestamped backup branch, tree-identical integrity verification, and the package.json+CHANGELOG-only bump check. Use when a release bump is no longer the latest commit and needs to be again.
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.