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...
version-bump
by linq2dbBump product and EF versions in Directory.Build.props to match the next release milestone. Creates an infra/bump-versions branch from origin/master, sets <Version> to the next milestone version, increments each <EFxVersion> minor by 1, and requires explicit user confirmation of the proposed diff.
kb-build
by linq2dbBuild the persistent knowledge base under .claude/knowledge-base/ from scratch. Phased and resumable — each step is sized to a single session and gates on coverage. Re-running picks up at the first incomplete step. Spawns kb-architect / kb-historian / kb-github-curator / kb-issue-detector as needed; never invents content directly.
fix-issue
by linq2dbOrchestrate the full workflow for reproducing and testing a linq2db issue — load the issue, clarify scope, create an `issue/<n>-<slug>` branch, and drive a regression test through test-writer + test-runner. Does not write the fix code itself; the user drives the fix and the skill wraps the testing loop around it.
find-issues
by linq2dbSearch linq2db/linq2db for issues and PRs matching a topic or the content of an existing ticket. Read-only. Topic mode takes a free-form query and returns ranked candidates. Ticket mode takes an issue/PR number or URL, derives search terms from its title/body/labels, excludes self, and returns candidates with a duplicate verdict. In ticket mode when the verdict is "likely duplicate", suggests /merge-duplicates as follow-up.
test-providers
by linq2dbConfigure the local test environment — enable / disable test-provider entries in `UserDataProviders.json` per TFM bucket, manage the docker containers those providers need (start / stop / setup-script run), and reset the file from `UserDataProviders.json.template`. Owns every edit to `UserDataProviders.json` and every `docker start` / `docker stop` call across the `.claude/` toolset; `/test` and `test-runner` consume the resulting state read-only.
test
by linq2dbWrite a new linq2db test, run an existing test / filter, or both. Orchestrates the `test-writer` and `test-runner` agents and reports a single pass/fail summary at the end. Env management (docker containers, `UserDataProviders.json`) lives in the `/test-providers` skill, not here — `/test` reads the configured state but never edits it.
verify-review
by linq2dbRe-verify prior `/review-pr` findings on a linq2db PR against the current PR HEAD. Collects all prior reviews, parses findings by severity-ID, reruns code-reviewer and baselines-reviewer in `verify` mode, then applies in-place edits (checkbox flips on prior review bodies, annotations + thread resolves on prior review comments) and posts a new draft review for partial fixes and new findings. Requires explicit user confirmation before any GitHub write.
kb-ask
by linq2dbFree-form Q&A grounded in the linq2db knowledge base. Spawns the kb-research subagent with a curated KB-doc shortlist; the agent answers from KB only and returns a synthesized answer plus citations. Optionally fetches full GitHub issue/PR bodies on demand. Read-only; never modifies the repo or the KB.
kb-issues
by linq2dbQuery and act on the detected-issues store under .claude/knowledge-base/detected-issues/. Filter via the shared selection grammar (random N, by area, severity, category, source, file, status). Per-result actions include showing detail, creating a GitHub issue (delegates to /create-issue), driving /fix-issue, marking wontfix / duplicate / dismissed / triaged.
kb-status
by linq2dbRead-only status report for the knowledge base — phase progress, source freshness (per-source cursor age), counts (areas, decisions, detected-issues by severity, GH index sizes), staleness summary (files with last_verified older than 90 days), and the last few entries from audit-log.md.
kb-refresh
by linq2dbIncremental update for the knowledge base under .claude/knowledge-base/. Reads cursors, fetches deltas (commits, GH issues/PRs/discussions, wiki, code), re-runs the relevant indexer agents scoped to the deltas, and runs a random-sample citation audit. Atomically advances cursors. Interruptible at every cursor boundary.
update-slnx
by linq2dbSync the /.claude/* virtual folders in linq2db.slnx with the actual on-disk contents of the .claude/ directory. Use after adding, removing, or renaming files or subdirectories under .claude/ (skills, hooks, local settings, etc.).
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.