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...
agent-readiness
by JKHeadleyScore a task or workflow on its coordination-vs-judgment ratio to tell whether it's a good agent candidate (EXO 3.0 task-decomposition matrix).
build
by JKHeadleyRigorous build process with worktree isolation, structured phases, quality gates, layered testing, observability, and self-improvement. Use for any substantial build task. Structurally enforced via stop hook.
instar-project
by JKHeadleyRegister, inspect, and drive multi-spec projects via the instar /projects API. Twelve subcommands cover the full Phase 1 surface — create / status / next / advance / drift / run-round / halt / ack / resume / abandon / accept-partial / claim-ownership.
test-as-self
by JKHeadleyDeploy the current instar dist into a throwaway agent home and verify the deploy is healthy — clean evidence instead of post-hoc log forensics. Use BEFORE shipping a change that touches the deploy/lifeline/server path; AFTER landing such a change; or to reproduce a crash observed in the wild.
agent-identity
by JKHeadleySet up persistent agent identity files (AGENT.md, USER.md, MEMORY.md) and teach the agent to read them at every session start. Works in any Claude Code project — no external dependencies required. Trigger words: who am I, identity, remember me, agent name, my principles, who are you, what are my values.
instar-dev
by JKHeadleyInstar-specific development skill used by the instar-developing agent (Echo, or any agent assigned instar-dev responsibilities). Wraps /build with mandatory side-effects review, signal-vs-authority principle check, and artifact generation. Structural enforcement via pre-commit/pre-push hooks — the instar repo refuses commits and pushes that didn't come through this skill. NOT a user-facing skill — end users should never invoke it.
instar-identity
by JKHeadleyEstablish and recover persistent agent identity that survives context compaction, session restarts, and autonomous operation. Use when an agent needs to know who it is, recover after context compression, orient at session start, or understand the identity infrastructure. Trigger words: who am I, remember, identity, after restart, compaction, context loss, who am I working with, my principles.
instar-session
by JKHeadleySpawn, monitor, and communicate with persistent Claude Code sessions running in the background. Use when a task needs to run without blocking the current session, when the user asks to do something in the background, or when a long-running task needs its own context window. Trigger words: background task, spawn session, persistent, run in background, parallel, separate session, async task.
instar-telegram
by JKHeadleySend and receive messages via Telegram for two-way agent communication. Use when the agent needs to notify the user, alert them about something, relay a response, or when Telegram messaging is the requested channel. Trigger words: send message, Telegram, notify, alert user, message me, ping me, let me know, reach out.
iterative-converging-audit
by JKHeadleyRun any "find all instances of X" sweep — a security audit, a safety audit, a code review, a research question, a compliance check — as an iterative loop that does NOT stop at one pass. Audit → fix → RE-audit → … until a clean pass returns zero NEW discoveries. Use whenever thoroughness matters and a single pass would miss things. Trigger words: audit, sweep, find all, review everything, comprehensive, thorough, exhaustive, security review, safety audit, no stone unturned, did we get everything, convergence.
command-guard
by JKHeadleySet up a PreToolUse hook in .claude/settings.json that blocks dangerous commands — rm -rf, force push, database drops, and others — before they execute. Teaches the pattern of safety hooks for any Claude Code project. Trigger words: safety, guard, block dangerous, protect, prevent destructive, safe mode, dangerous commands, risky operations.
smart-web-fetch
by JKHeadleyFetch web content efficiently by checking llms.txt first, then Cloudflare markdown endpoints, then falling back to HTML. Reduces token usage by 80% on sites that support clean markdown delivery. No external dependencies — installs a single Python script. Trigger words: fetch URL, web content, read website, scrape page, download page, get webpage, read this link.
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.