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.
Querying local SQLite index...
rivet-rule
by pulseengineFor rivet projects — run `rivet validate` / `rivet close-gaps` and act on the diagnostics yourself. Rivet is a mechanical oracle; the closure decisions are yours per the project-scaffolded prompts.
release-planning
by pulseengineThis skill should be used to plan releases in rivet — assign which requirements/artifacts belong to which release and drive development from that plan — and to run the issue-driven delivery loop: an error, regression, or optimization comes in, gets evaluated, flows through the full verification chain, and ships in a planned release. Use it when scoping a release ("what goes in v0.X"), triaging an incoming issue/bug/optimization toward a release, asking "is v0.X ready to cut", or driving a roadmap. Builds on rivet's existing `release:` field + status lifecycle (draft→proposed→approved→implemented→verified→accepted); composes with the feature loop (build each item), traceability-audit (a release is ready when its items' V is closed), and release-execution (cut it).
stpa-audit
by pulseengineThis skill should be used when conducting or AUDITING an STPA (System-Theoretic Process Analysis) or STPA-Sec (its security extension) hazard analysis on a PulseEngine project — including "do an STPA", "audit the hazard analysis", "are our losses/hazards/UCAs complete", "STPA-Sec review", "security hazard analysis", "check the safety/ artifacts", or before a release whose V-model gate depends on the hazard analysis being sound. Fires whenever the front-end of the safety case (losses → hazards → constraints → UCAs → loss scenarios) needs to be built or verified. rivet already provides the typed schema, link semantics, and `rivet check` topology validation — this skill operationalizes the method and the audit on top of it.
bootstrap-verification
by pulseengineThis skill should be used to stand up the PulseEngine verification scaffolding for a NEW or not-yet-built piece — a greenfield repo, a fresh component, or work that doesn't exist yet — so it is traceable and verifiable from commit one rather than retrofitted later. Use it when starting something from scratch, when a repo/component has no rivet project yet, or when the user says "bootstrap this", "set this up properly from the start", "I want to use this on a piece I haven't done yet", or "get the verification scaffolding in". It picks the target standard(s), runs rivet init, scaffolds STPA/STPA-Sec + the traceability skeleton, seeds the top of the V, and wires the piece into the feature loop, release gate, and compliance/MC-DC reporting — the greenfield counterpart to pulseengine-feature-loop (which assumes an existing project).
oracle-gate-a-change
by pulseengineThis skill should be used whenever proposing, designing, landing, or evaluating a consequential change on a PulseEngine project (rivet, spar, witness, sigil, meld, loom, synth, wohl) — including "propose a change", "add a feature", "fix a bug", "is this safe to land", "what verifies this", "what's the gate", "how do we know this is correct", "before merging this", or whenever a code change needs a mechanical check to back it. ALWAYS use this skill before claiming a property holds and before recommending a change be merged.
pulseengine-feature-loop
by pulseengineThis skill should be used when doing a feature end-to-end on a PulseEngine project (rivet, spar, witness, sigil, meld, loom, synth, wohl, kiln) — including "implement a feature", "add a new requirement", "extend the architecture", "write a new pass", "ship a feature end-to-end", "do this properly with traceability", "model-driven implementation", or any feature work that should pass through the full AADL → WIT → typed traceability → oracle-gated code → MC/DC → attestation → verify loop. ALWAYS use this skill when the user authorizes feature work on a PulseEngine project and the work touches more than a single file.
report-tool-friction
by pulseengineThis skill should be used whenever a PulseEngine tool (rivet, spar, witness, sigil, meld, loom, synth, kiln, gale, scry, smithy, thrum, temper, mcp — the roster lives in the pulseengine-toolchain memory) produces friction during real work — it errors, crashes, produces wrong or surprising output, is missing a capability you needed, has confusing/undocumented behavior, or forced you into a workaround. ALWAYS use this skill the moment you notice yourself working *around* a tool instead of *with* it, or saying "this should just work but doesn't." The friction is the signal; capturing it as an issue in the tool's own repo is the action. Fires inside [`pulseengine-feature-loop`] and [`release-execution`] and any standalone tool use.
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.