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...
gsd-headless
by open-gsdOrchestrate GSD (Git Ship Done) projects programmatically via headless CLI. Use when an agent needs to create milestones from specs, execute dev workflows, monitor progress, check status, or control execution (pause/stop/skip/steer). Triggers on "run gsd", "create milestone", "execute project", "check gsd status", "orchestrate development", "run headless workflow", or building orchestrators that coordinate multiple GSD workers.
decompose-into-slices
by open-gsdBreak a plan or milestone brief into independently-grabbable vertical slices (tracer bullets), written to `M###-ROADMAP.md` by default (GitHub issues only on explicit confirmation). Prefers many thin slices over few thick ones and marks dependency order. Use when asked to "break this into slices", "decompose the plan", "vertical slices", "break into issues", or when a plan needs task-level decomposition.
grill-me
by open-gsdRelentless sequential interview that stress-tests a plan or design until every decision branch is resolved. Use when the user wants to "grill me", "stress-test the plan", "interrogate my design", "resolve the decision tree", or whenever a plan feels hand-wavy, under-specified, or carries hidden coupling that planning phases must surface before execution. Pairs with the discuss phase and blocks execution until alignment is reached.
write-milestone-brief
by open-gsdSynthesize the current conversation into a milestone brief (PRD). Writes to `M###-CONTEXT.md` by default, or files a GitHub issue only with explicit user confirmation. Use when asked to "turn this into a PRD", "draft a milestone brief", "capture this context", "write it up", or when enough has been discussed to commit the plan to paper. Does not interview — it synthesizes what is already known.
handoff
by open-gsdPrepare a clean cross-session handoff so the next agent can pick up where you left off. Writes a focused `continue.md` in the active slice dir and ensures `STATE.md` + summary artifacts are current. Use when asked to "hand off", "prepare handoff", "pause work", "bookmark this", "I'll come back to this later", before running out of context budget, or ending a long session with unfinished work.
gh
by open-gsdInstall and configure the GitHub CLI (gh) for AI agent environments where gh may not be pre-installed and git remotes use local proxies instead of github.com. Provides auto-install script with SHA256 verification and GITHUB_TOKEN auth with anonymous fallback. Use when gh command not found, shutil.which("gh") returns None, need GitHub API access (issues, PRs, releases, workflow runs), or repository operations fail with "failed to determine base repo" error. Documents required -R flag for all gh commands in proxy environments. Includes project management: GitHub Projects V2 (gh project), milestones (REST API), issue stories (lifecycle and templates), and label taxonomy management.
github-workflows
by open-gsdWork with GitHub Actions CI/CD workflows - read live syntax, monitor runs, and debug failures. Use when writing, running, or debugging GitHub Actions workflows.
make-interfaces-feel-better
by open-gsdDesign-engineering principles for making interfaces feel polished. Use when building UI components or doing visual detail work — animations, hover states, shadows, borders, typography, micro-interactions. Triggers on "make it feel better", "feels off", stagger animations, border radius, optical alignment, font smoothing, tabular numbers, box shadows.
write-docs
by open-gsdCollaborative authoring workflow for proposals, technical specs, decision docs, READMEs, ADRs, and long-form prose that must work for fresh readers. Three stages: gather context, iterate on structure, reader-test for a stranger. Use when asked to "write the docs", "draft a proposal", "write a spec", "write an RFC", "write the README", or when a doc must be understandable without this session's context.
tdd
by open-gsdTest-driven development with red-green-refactor loops around vertical slices (tracer bullets), not horizontal layers. Use when asked to "use TDD", "write test-first", "red-green-refactor", "build this with tests", or when a feature has a clear observable contract. The discipline complement to the test/add-tests skills (the mechanics).
spike-wrap-up
by open-gsdPackage findings from a completed spike into a durable project-local skill that auto-loads on future similar work. Reads the latest `.gsd/workflows/spikes/` dir, interviews the user on what's reusable, then writes `.agents/skills/<name>/SKILL.md`. Use when asked to "wrap up the spike", "package this as a skill", "make this reusable", "turn findings into a skill", or at the synthesize phase of `/gsd start spike`.
security-review
by open-gsdThreat-model-driven security review of a change, feature, or subsystem. Runs a STRIDE-style pass over the actual code and produces a filing-ready report with severity, exploit scenario, and remediation. Use when asked to "security review", "threat model", "check for vulnerabilities", "audit for security", "secure this", or before shipping changes touching auth, input handling, data access, or external surfaces.
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.