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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btw
by gsd-buildAsk a quick side question about your current work without derailing the main task. Answers from existing conversation context only — no tool calls, no file reads, single concise response. Use when you need a fast answer from what is already in this session.
verify-before-complete
by gsd-buildBlock completion claims until verification evidence has been produced in the current message. Use before marking a task/slice/milestone complete, before creating a commit or PR, before saying "it works" or "tests pass", and any time you are about to claim work is done. The rule is: evidence before claims, always — running the verification must happen now, not "earlier in the session". Fresh output or no claim.
dependency-upgrade
by gsd-buildPlan, batch, and verify dependency upgrades safely. Triages outdated packages into risk tiers, upgrades in order (dev/minor/patch first, runtime majors last), verifies each batch before moving on, and produces an auditable commit sequence. Use when asked to "upgrade deps", "bump packages", "update node_modules", "fix vulnerabilities", "upgrade React/Node/TypeScript", or after `/gsd start dep-upgrade`. Complements the dep-upgrade workflow template with execution-level rigor.
decompose-into-slices
by gsd-buildBreak a plan or milestone brief into independently-grabbable vertical slices (tracer bullets). Produces slices in `M###-ROADMAP.md` by default, or GitHub issues only with explicit user confirmation. Use when asked to "break this into slices", "decompose the plan", "vertical slices", "break into issues", or when a plan is ready but needs task-level decomposition. Prefers many thin slices over few thick ones; marks dependency order explicitly.
debug-like-expert
by gsd-buildDeep analysis debugging mode for complex issues. Activates methodical investigation protocol with evidence gathering, hypothesis testing, and rigorous verification. Use when standard troubleshooting fails or when issues require systematic root cause analysis.
api-design
by gsd-buildDesign or review an HTTP/REST/GraphQL API for versioning, pagination, error shapes, idempotency, auth, and evolvability. Use when asked to "design an API", "shape the endpoints", "design the schema", "add a new endpoint", "review this API", or when building/modifying a public or internal HTTP surface. Complements `design-an-interface` (which is interface-agnostic) by covering HTTP-specific concerns like status codes, cache headers, and breaking-change management.
design-an-interface
by gsd-buildProduce 3+ radically different designs for a module, API, or interface, compare them in prose, and synthesize a recommendation. Use when asked to "design an interface", "shape this API", "design it twice", "explore module boundaries", or when planning a new deep module and the first idea is unlikely to be the best. Based on "Design It Twice" from A Philosophy of Software Design — the value is the contrast, not the first draft.
forensics
by gsd-buildPost-mortem a failed GSD auto-mode run. Traces from symptom to root cause using `.gsd/activity/*.jsonl`, `.gsd/journal/YYYY-MM-DD.jsonl`, `.gsd/metrics.json`, and `.gsd/auto.lock`. Produces a filing-ready bug report with file:line references and a concrete fix suggestion. Use when asked to "forensics", "post-mortem", "why did auto-mode fail", "trace the stuck loop", "debug the crash", after `/gsd forensics` is invoked, or when a session ended in an unexpected terminal state. Reads existing artifacts — does NOT re-run anything.
gh
by gsd-buildInstall 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.
grill-me
by gsd-buildRelentless 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.
gsd-orchestrator
by gsd-buildBuild software products autonomously via GSD headless mode. Handles the full lifecycle: write a spec, launch a build, poll for completion, handle blockers, track costs, and verify the result. Use when asked to "build something", "create a project", "run gsd", "check build status", or any task that requires autonomous software development via subprocess.
gsd-headless
by gsd-buildOrchestrate GSD (Get Shit Done) projects programmatically via headless CLI. Use when an agent needs to create milestones from specs, execute software development workflows, monitor task progress, check project status, or control GSD execution (pause/stop/skip/steer). Triggers on requests to "run gsd", "create milestone", "execute project", "check gsd status", "orchestrate development", "run headless workflow", or any programmatic interaction with the GSD project management system. Essential for building orchestrators that coordinate multiple GSD workers.
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.