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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wr-itil-capture-rfc
by windyroadLightweight RFC-capture skill for aside-invocation during foreground work — mandatory problem-trace per ADR-060 I1 invariant, skeleton RFC file, single commit per capture, no inline README refresh. Defers full duplicate analysis and README refresh to /wr-itil:manage-rfc. Use this when the user (or agent) wants to capture an RFC quickly with a clear problem trace. For full lifecycle management, use /wr-itil:manage-rfc.
wr-itil-capture-story-map
by windyroadLightweight story-map-capture skill for aside-invocation during foreground work — mandatory leading problem-trace AND JTBD-trace per ADR-060 I3 + I4 invariants, skeleton HTML file at `docs/story-maps/draft/STORY-MAP-NNN-<slug>.html` per ADR-060 § Phase 2 encoding amendment 2026-05-12, single commit per capture, no inline README refresh. Defers full backbone/ribs/slices authoring + lifecycle transitions to /wr-itil:manage-story-map. Use when the user (or agent) wants to capture a new story-map quickly with clear problem + JTBD anchoring.
wr-itil-capture-story
by windyroadLightweight story-capture skill for aside-invocation during foreground work — mandatory leading problem-trace AND JTBD-trace per ADR-060 I6 + I9 invariants, optional `--rfc` and `--story-map` flag args (I7 + I8 enforce at `accepted` transition not at capture), skeleton story file at `docs/stories/draft/STORY-NNN-<slug>.md`, single commit per capture, no inline README refresh. Defers full INVEST shape + acceptance transition to /wr-itil:manage-story. Use when the user (or agent) wants to capture a story quickly with clear problem + JTBD anchoring. For full lifecycle management, use /wr-itil:manage-story.
wr-itil-manage-story-map
by windyroadHeavyweight story-map intake + lifecycle management following ADR-060 Phase 2. Authors backbone × ribs × slices structure on draft maps, transitions through draft → accepted → in-progress → completed → archived, re-validates I3 + I4 invariants at every transition, and refreshes docs/story-maps/README.md per the P062 / P094 contract pattern. Companion to /wr-itil:capture-story-map (lightweight aside surface).
wr-itil-manage-story
by windyroadHeavyweight story intake + lifecycle management following ADR-060 Phase 2. Creates and updates story tickets, transitions through draft → accepted → in-progress → done → archived lifecycle, enforces I7 + I8 trace-gate at the accepted transition, runs INVEST checks per I10 at acceptance, auto-transitions draft→in-progress on first non-capture commit and in-progress→done on all-criteria-ticked + linked RFC closes, and refreshes docs/stories/README.md per the P062 / P094 contract pattern. Companion to /wr-itil:capture-story (lightweight aside surface).
wr-itil-transition-problems
by windyroadBatch-advance multiple problem tickets through the lifecycle in one invocation — Open → Known Error, Known Error → Verification Pending, Verification Pending → Closed. Loops the per-ticket /wr-itil:transition-problem mechanic (rename, Status edit, P057 re-stage, P063 external-root-cause detection, P062 README refresh) without paying N× SKILL.md reload latency or violating split-skill execution ownership. Produces ONE shared commit covering all surviving transitions per ADR-014 batch-grain. Use when closing the Verification Queue at the end of a `/wr-retrospective:run-retro` Step 4a pass, batch-closing release-aged verifyings during `/wr-itil:work-problems` AFK orchestration, or confirming multiple Step 9d verifications in `/wr-itil:manage-problem review`. Singular sibling — `/wr-itil:transition-problem` (one ticket per invocation).
wr-retrospective-analyze-context
by windyroadDeep context-usage analyzer. Runs richer heuristics than run-retro Step 2c — per-turn attribution, per-plugin decomposition, suggestion generation, policy-breach detection. Produces a markdown report at docs/retros/<date>-context-analysis.md with an HTML-comment trailer carrying the bucket-snapshot for delta-from-prior comparison. Auto-fires from run-retro Step 2c when the combined trigger holds (calendar-elapse >14 days OR delta >20% any bucket, once-per-day guard) per ADR-043 Amendment 2026-06-08; also user-invokable on demand.
wr-itil-manage-rfc
by windyroadHeavyweight RFC intake + lifecycle management following the ADR-060 Problem-RFC-Story framework. Creates new RFCs (delegates to /wr-itil:capture-rfc for the lightweight path), updates existing RFCs, transitions through proposed → accepted → in-progress → verifying → closed lifecycle, runs WSJF re-rank reviews, and refreshes docs/rfcs/README.md per the P062 / P094 contract pattern.
wr-itil-list-stories
by windyroadList INVEST-shaped story tickets from docs/stories/ as a markdown table. Read-only display — no edits, no interaction. Optional `--rfc RFC-<NNN>` filter to surface a specific RFC's ordered story list per ADR-060 Phase 2.
wr-itil-reconcile-stories
by windyroadDetect and correct drift between docs/stories/README.md and the on-disk story inventory. Wraps the diagnose-only packages/itil/scripts/reconcile-stories.sh script with an agent-applied-edits pattern that preserves narrative content (the "Last reviewed" prose paragraph). Use when docs/stories/README.md Story Rankings or Done sections drift from filesystem state — typically detected by manage-story Step 0 preflight or work-problems preflight on RFC iters with story-tier traces.
wr-blog-create-social-posts
by windyroadGenerate platform-specific social posts for a published windyroad blog article. Covers LinkedIn, Twitter, Bluesky, Hacker News, Lobsters, Reddit, and dev.to. Applies the windyroad voice gate, content-risk gate, SW-critic loop, and cognitive-accessibility gate per platform.
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.