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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vault-semantic-search
by allenhutchisonSearch vault notes by meaning using semantic search (RAG). Activate this skill when users want to find notes by concept or topic rather than exact keywords, or when keyword search tools return poor results.
architecture-audit
by allenhutchisonWalk the codebase looking for TypeScript technical debt — oversized files, DRY violations, dead code, missing tests, sloppy typing (`any`/`@ts-ignore`), and weak abstractions. Categorize each finding into a discrete unit of work; open a focused PR for mechanically-safe fixes and file a GitHub issue for refactors that need design discussion. One PR or one issue per finding — never bundled. Use when the user asks to "audit the architecture", "find tech debt", "look for code smells", "do an architecture sweep", or when invoked nightly by a scheduled remote agent. Has working-tree side effects (branches + PRs) and GitHub side effects (issues, labels). Quiet-day result is "codebase looks good" with no PR or issue — that's a valid outcome.
audit-docs
by allenhutchisonWalk every user-facing doc in `docs/guide/`, `docs/reference/`, plus `README.md` and `AGENTS.md`, and validate each factual claim against the code (settings names + defaults, command-palette IDs, file paths, schedule formats, tool names). Patch drift in place; add new docs only for user-visible features that aren't covered. Use when the user asks to "audit the docs", "review the docs", "validate docs against the code", "find doc gaps", "sync docs with the codebase", or similar. Writes drift fixes + new docs to the working tree and stops; the caller (a human, or the `daily-update` meta-skill) is responsible for committing and opening a PR.
code-review
by allenhutchisonReview code for quality, correctness, and maintainability. Use this skill when reviewing pull requests, auditing existing code, refactoring for clarity, or enforcing coding standards. Covers DRY principles, SOLID design, error handling, performance, security, testing, and TypeScript/JavaScript best practices. For Obsidian plugin-specific API guidance, refer to the obsidian-plugin-development skill.
create-pr
by allenhutchisonCreate a pull request for this repository. Enforces the PR template, runs all CI checks locally before pushing, and ensures documentation is updated. Use this skill whenever preparing code changes for review.
daily-changelog
by allenhutchisonGenerate a daily changelog from the PRs that merged on a given date and write it to `planning/changelog/YYYY-MM-DD.md`. Use when the user asks for "the daily changelog", "what shipped today/yesterday", "what landed on <date>", "write up today's changes", "summarize the day's PRs", or any variant of "what merged on <date>". Defaults to yesterday when no date is given. Distinct from `src/release-notes.json` (per-version, user-facing) and `docs/changelog.md` (the rendered version stream) — this is an internal day-by-day record. Always writes one markdown file per day to `planning/changelog/`.
daily-update
by allenhutchisonRun obsidian-gemini's per-day housekeeping skills (daily-changelog, audit-docs, triage-issues) and package whatever they wrote into one PR. Use when the user asks to "do the daily update", "run the daily skills", "morning sweep", "end-of-day cleanup", or when invoked by a scheduled remote agent doing the daily run. Bundles the per-day skills behind one entry point so a single /schedule slot covers the whole routine. Safe to invoke manually at any time.
eval-harness
by allenhutchisonRun the eval harness against a real Obsidian instance, monitor for the harness's known reliability gotchas (concurrent runs, CLI-bridge hangs, stale child processes), and bless a result as a baseline only when the run is clean. Use when the user asks to "run the evals", "bless a baseline", "measure the impact of <change> on solve rate", "run a model sweep", or similar. Has Obsidian-CLI side effects (drives the agent view, creates eval-scratch fixtures, may set chatModelName for the duration of the run); does NOT modify plugin source code or commit unless explicitly asked.
gemini-api-dev
by allenhutchisonUse this skill when building applications with Gemini models, Gemini API, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API capabilities.
obsidian-cli
by allenhutchisonUse the Obsidian CLI to debug, inspect, and test Obsidian plugins during development. Covers plugin reloading, console inspection, runtime evaluation, driving the UI (commands, CDP, screenshots, mobile emulation), frontmatter properties, and common debugging recipes for the gemini-scribe plugin.
obsidian-plugin-development
by allenhutchisonBuild, modify, and debug Obsidian plugins using the TypeScript API. Use this skill when working with Obsidian plugin source code, the obsidian npm package, plugin UI (views, modals, settings, commands, ribbons), vault file operations, editor manipulation, workspace management, metadata cache, events, markdown rendering, or the Obsidian CLI. Covers plugin lifecycle, best practices, common patterns, and the full TypeScript API surface.
plugin-test
by allenhutchisonThree-pass acceptance test for the obsidian-gemini plugin — unit tests, then UI/state via the Obsidian CLI (cheap pass), then API-spending verification (only with explicit user authorization). Driven by the user-facing docs as the source of truth for what should work, with extra focus on functionality shipped since the last release. The agent acts as judge between passes; later passes only run when the earlier ones pass cleanly. Use when the user asks to "test the plugin", "smoke test the release", "verify before release", "run the pre-release tests", "act as a judge on the plugin", or similar. Has Obsidian-CLI side effects (modal opens, plugin reloads, screenshots) but does NOT modify source code or commit; reports go to the working tree under `planning/test-reports/`.
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.