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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repo-map-wiki
by e128Build a pyramid-structured wiki for a product area by exploring its source repos. Produces Tufte Dracula HTML docs with Mermaid diagrams.
fix-ci
by e128Fix CI/build errors from pasted output. Two modes: (1) Standard — parses pasted compiler errors, analyzer violations, or test failures, applies targeted fix, confirms with build-validator. (2) Divergence — for errors that pass locally but fail CI; cross-references local-vs-ci-build.md to explain the cause, then applies the fix. Accepts $ARGUMENTS or message body. Triggers on: fix these, fix this, fix this error, fix these errors, CI failed, build errors, fix: [pasted errors], compiler error, test failed, here are the errors, these need fixing, error output from CI, works locally not CI, CI failed but local passes, github actions failed, why does CI fail, CI only failure, fix CI divergence. Not for: running a new warning scan on recently modified files (use build-validator).
learn
by e128Researches a topic from the web and adds it to the project's lode/ documentation. Use /learn <topic> to research official docs and persist findings. Supports --skill flag to output as a SKILL.md instead. Triggers on: learn about, research topic, add to lode from web, learn this, deep research.
lode-audit
by e128Audits lode/ documentation for content accuracy — checks whether each doc correctly describes the current codebase. Distinct from lode-sync (which only checks timestamps). Produces a staleness table with git-verified findings, then makes targeted updates only on the sections you approve. Use after large refactors, renames, or when the lode feels out of sync with the code. Triggers on: audit lode, lode out of date, lode accuracy check, is the lode correct, lode stale, lode content review, audit documentation, check lode, lode drift, lode wrong, lode inaccurate, lode needs correction. Not for: updating a single known lode file (just edit it directly), or checking timestamps only (use lode-sync agent).
lode-capture
by e128Capture a single insight into the lode/ documentation store. Routes the insight to the correct lode file based on topic, writes it as a current-state fact, and timestamps the file. Lightweight alternative to lode-sync for one-off captures. Triggers on: save this to the lode, capture this insight, capture to lode, remember this for next time, save this knowledge, capture that knowledge.
lode
by e128Quick lookup in the lode/ documentation store. Searches for a topic across all lode files and returns matching content with context. Fast alternative to manually grepping the documentation tree. Triggers on: lode search, search lode, find in lode, lode lookup, what does lode say about, check lode for, lode docs.
review-orchestrator
by e128Orchestrates all code review agents to audit recent .NET changes. Accepts --commits N or --days N to define review scope, discovers agents dynamically, runs them in parallel, and produces a severity-grouped report with actionable recommendations. Triggers on: code review, review code, review changes, audit code, check code quality, clanker check, state bloat, grab bag review, check for flag bloat, mutation review, clanker discipline.
script-discovery
by e128Find repeated behaviors in Claude Code sessions that should be deterministic bash scripts. A "deterministic script" replaces an ad-hoc multi-turn Claude behavior with a repeatable, token-free automation (bash script + optional hook + optional keyword shortcut). Scores candidates on token-savings potential and builds the top N if approved. Supports --scan-skills mode to statically analyze agent/skill files for inline bash that should be extracted to scripts. Triggers on: script discovery, find script candidates, what should be a script, repeated patterns, deterministic scripts, automate patterns, session patterns, discover scripts, what keeps repeating, find automations, script opportunities, review agents for scripts, extract scripts from skills, deterministic portions. Not for: token optimization plans (use token-optimizer), weekly session analysis (use weekly-learner), or single-session review (use token-optimizer --current).
solution-audit
by e128Audit .NET solution health across 10 dimensions: dependency graph, solution sync, CPM compliance, package health, framework consistency, IVT & encapsulation, build config, analyzer config, NuGet config, and suppression hygiene. Works with any .NET solution (.slnx or .sln). Parses all config files once, spawns 3 parallel agents, and produces a severity-grouped report with a Mermaid dependency graph. Triggers on: solution audit, audit solution, project graph, dependency audit, solution health, audit projects, check solution.
strategy-audit
by e128Strategic alignment audit for codebases. Applies Roger Martin's strategy framework directly to code — analyzing what the code reveals about its strategic choices, investment levels, and capability reinforcement. Produces a prioritized findings report and feeds actionable refactorings into dev-planning. Use for code review through a strategy lens, feature gap analysis, investment mismatch detection, or surfacing undocumented strategic choices. Triggers on: strategy audit, strategic audit, martinize, martinizing, roger martin, code as strategy, strategy review, investment mismatch, capability analysis, strategic alignment, where should I invest, code investment audit, feature gap analysis, over-engineered, dead code strategy, what's table stakes.
tech-debt-audit
by e128Tech debt and architecture audit for .NET repos. Produces plans/TECH_DEBT_AUDIT.md with file-cited findings, severity, and effort estimates. Supports module scoping and focused-mode shortcuts (crap, dead-code, duplicates, suppressions, solid) for single-dimension audits. Does not auto-invoke. Triggers on: tech debt audit, debt audit, codebase health check, architecture review, code quality assessment, audit tech debt, tech debt scan, code health, crap analysis, crap score, dead code audit, find dead code, unused types, dup-scan, code duplication, duplicate code, find duplicates, DRY violations, review code suppressions, audit pragma warnings, pragma warning disable, suppression cleanup, code hygiene, SOLID violations, solid review.
threat-model
by e128STRIDE threat modeling with CAPEC drill-down and DREAD-lite scoring. Builds a DFD from lode/ domain knowledge, identifies threats per trust boundary, maps to concrete attack patterns, and produces a prioritized threat register.
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.