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...
debug-lldb
by regenrekCapture and analyze thread backtraces with LLDB/GDB to debug hangs, deadlocks, UI freezes, IPC stalls, or high-CPU loops across any language or project. Use when an app becomes unresponsive, switching contexts stalls, or you need thread stacks to locate lock inversion or blocking calls.
peky
by regenrekUse when operating peky from the CLI or TUI, especially for AI agents who need reliable, low-error procedures. Covers how to target sessions/panes correctly, use scopes, avoid confirmation prompts, and keep CLI/TUI/daemon in sync.
plugin-id
by regenrek__PLUGIN_DESCRIPTION__. Use when [describe trigger scenarios].
debug
by regenrekTraces failures from observed symptoms to the first incorrect state transition.
review
by regenrekReviews TypeScript and agent workflow changes with strict risk focus.
planr-fix
by regenrekImplement remaining scoped work in this repository to verified completion. Use for direct bug fixes, regressions, failing tests, `planr-review` findings, or unfinished `.planr` plan phases when the next step is to change code, tests, or docs, keep live `.planr` status honest, and prove the result. Not for writing a new execution contract (`planr-plan`), giving a verdict-only status answer (`planr-status`), or running a findings-first audit (`planr-review`).
planr-plan
by regenrekCreate or update executable `.planr/plans/*.plan.md` contracts in this repository. Use when scope, ownership, phase breakdown, verification, or acceptance criteria must be defined before implementation, including bug-to-plan conversions and review-finding follow-up plans. Not for executing fixes (`planr-fix`), giving a verdict-only status answer (`planr-status`), or running a findings-first audit (`planr-review`).
planr-review
by regenrekReview agent-owned implementation scope in this repository against `.planr` plans or live status, path-scoped Git evidence, and acceptance criteria. Use for findings-first audits of completion, correctness, architecture, hard-cut cleanup, and test sufficiency. Not for implementing fixes (`planr-fix`), writing a new execution contract (`planr-plan`), or giving a verdict-only status answer (`planr-status`).
planr-status
by regenrekAssess the honest current state of a scoped `.planr` task in this repository. Use when the user asks what is done, what remains, what is blocked, whether a scope is complete, which scopes are open, or what should come next. Start with the deterministic `.planr` CLI where it has command coverage. Not for implementing fixes (`planr-fix`) or running a findings-first audit (`planr-review`).
planr-summary
by regenrekProduce a user-facing recap of an owned `.planr` scope in this repository. Use when the user asks what changed, why it changed, what works now, what intentionally no longer works, or what remains blocked after `planr-fix`, `planr-status`, or `planr-review`. Not for deciding completion (`planr-status`), running a findings-first audit (`planr-review`), or continuing implementation (`planr-fix`).
npm-trustme
by regenrekAutomate npm Trusted Publisher setup via the npm-trustme CLI. Use when configuring or verifying npm Trusted Publishers for GitHub Actions with npx npm-trustme, including browser automation and WebAuthn passkey approval.
release-npm-trustme
by regenrekRelease automation for npm-trustme. Use when asked to cut a new npm-trustme version, run the release script, or tag/publish a new release.
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.