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...
refactor-recommender
by fjpulidopsr:refactor-recommender — Scan the codebase for refactoring opportunities ranked by impact/effort ratio. Optionally creates GitHub Issues for tracking.
get-backlog-specs
by fjpulidopsr:get-backlog-specs — View product-driven backlog from GitHub Issues and propose top 3 for implementation.
health-check
by fjpulidopRun a comprehensive codebase health check — tests, linting, coverage, complexity, and dependency audit. Compare with previous runs to detect regressions.
memory-inspect
by fjpulidopInspect and manage agent memory directories. Lists all sr-* agent memory stores, shows per-agent stats (file count, size, last modified), displays recent entries, and detects stale or orphaned files.
propose-spec
by fjpulidopExplore a spec idea and produce a structured proposal
vpc-drift
by fjpulidopDetect when user personas defined in the VPC are drifting from actual usage patterns. Compares persona Jobs/Pains/Gains against the product backlog, implemented features, and agent memory to surface alignment gaps and recommend VPC updates.
why
by fjpulidopsr:why — Search explanation records written by specrails agents during the OpenSpec implementation pipeline.
batch-implement
by fjpulidopRun the implement pipeline over multiple backlog tickets in one session. Per ticket: spawn architect → spawn developer → spawn reviewer (the same three-phase pipeline $implement runs), then move to the next. Sequential by default; parallel only when the user explicitly opts in AND the tickets are independent. Reports an aggregated verdict at the end. Use when the user invokes `$batch-implement #N #M #K` or `$batch-implement --status todo`.
merge-resolve
by fjpulidopUser-facing entry point for resolving git merge conflicts. Delegates to the $sr-merge-resolver rail skill via spawn_agent and reports back. Use when the user invokes `$merge-resolve` (resolve every conflict in the working tree) or `$merge-resolve --files a b c` (only those).
sr-frontend-reviewer
by fjpulidopFrontend-specialist reviewer for the specrails implement pipeline. Use when the developer changed UI surfaces. Validates UI behaviour, accessibility, keyboard reachability, responsive layout, and design-token usage on top of the standard sr-reviewer checks. Findings-only — never modifies code. Invoked via $sr-frontend-reviewer.
sr-product-analyst
by fjpulidopProduct analyst for the specrails workflow. Reads the current state of the backlog (.specrails/local-tickets.json) and the codebase, then reports on coverage, drift, and recommended next moves. Does NOT propose new tickets (that's sr-product-manager) and does NOT implement. Invoked via $sr-product-analyst.
sr-reviewer
by fjpulidopReviewer role for the specrails implement pipeline. Validates the entire implementation: the OpenSpec change package (proposal/design/tasks/specs) is well-formed, the developer's code matches the design's public API and invariants, every tasks.md box is ticked, the tests cover every spec scenario, and the project's full test/build suite passes. Writes a confidence-score.json artefact. Does NOT modify the developer's code. Invoked via $sr-reviewer.
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.