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...
retrospective
by OysteinAmundsenPost-task "lessons learned" review. Evaluates what worked, what didn't, and proposes updates to instruction and skill files to continuously improve AI-assisted development workflows.
astro-demo
by OysteinAmundsenCreate an interactive Astro demo component and its MDX docs page for a grid feature or plugin. Scaffolds .astro demo files following project conventions. Use when adding a live/interactive example; for updating existing prose or API-reference docs, use the docs-update skill instead.
bench
by OysteinAmundsenRun @toolbox-web benchmarks as local regression testing during development — either current-vs-previous-run (`bun run bench`) or current-vs-tag (the bundled bench-vs-tag.ts orchestrator). Covers writing `.bench.ts` files as part of issue development, running the suites, locating artifacts, and interpreting regressions. Use when implementing a perf-sensitive change, before opening a PR that touches hot paths, or when asked to benchmark.
bundle-check
by OysteinAmundsenCheck that the @toolbox-web/grid build stays within bundle size budget (index.js ≤170 kB raw, ≤50 kB gzipped hard limit, ≤45 kB gzipped soft warning). Run after any code change that could affect bundle size.
debug-browser
by OysteinAmundsenDebug or reproduce a component issue in a live browser using the Chrome DevTools MCP server. Use to get a live repro, inspect the DOM, watch console errors, analyze network requests, take screenshots, or evaluate scripts against a running grid. Covers launching dev servers and navigating to pages/stories. (For performance traces and hot-path profiling, use the debug-perf skill.)
debug-perf
by OysteinAmundsenInvestigate, optimize, and prevent performance regressions in @toolbox-web/grid. Use when something is slow, janky, or memory-leaking, when checking or guarding against a performance regression, when optimizing or improving a hot path, or when capturing/analyzing a performance trace. Covers Chrome DevTools MCP trace capture, the bundled trace analyzer, profiling, hot-path analysis, virtualization tuning, and render-scheduler optimization. (For micro-benchmark regression gating with .bench.ts, see the `bench` skill.)
docs-update
by OysteinAmundsenAuthor, restructure, or update documentation for the Astro/Starlight docs site. Covers ADDING a new docs page or route (MDX + sidebar registration in astro.config.mjs + llms.txt/llms-full.txt inclusion), which prose/MDX and API-reference docs to check after a code change, how to write them, the `<Audience>` and `llmsFull` curation knobs, and regenerating typedoc API docs. Use whenever you create, move, rename, or edit any file under apps/docs/, or after any feature, fix, or refactor that touches user-visible behaviour. (For building a new interactive demo component, use the astro-demo skill instead.)
new-adapter-feature
by OysteinAmundsenAdd a new feature OR backport a bugfix consistently across the Angular, React, and Vue adapter libraries for @toolbox-web/grid. Use whenever you touch one adapter, to confirm the other two need the same change and to keep full parity.
new-adapter
by OysteinAmundsenCreate a new framework adapter library for @toolbox-web/grid, with a matching demo app. Covers library scaffolding, adapter implementation, demo setup, workspace wiring, and e2e parity testing.
new-plugin
by OysteinAmundsenCreate a new grid plugin for @toolbox-web/grid following the canonical plugin structure. Use when adding a plugin or extending the grid with optional, toggleable behaviour (hooks, styles, tests, and documentation). Prefer a plugin over a core feature to keep the bundle lean.
pr-comments
by OysteinAmundsenRead, reply to, and resolve review-comment threads on a GitHub pull request using the gh CLI. Use whenever you need to triage PR feedback (fetch all unresolved review threads as structured JSON, post inline replies on specific threads, and mark threads resolved) instead of hand-writing a one-off gh/GraphQL script each time. Prefer this over the generic address-pr-comments skill for bulk thread triage and programmatic gh/GraphQL access.
qa-apply-findings
by OysteinAmundsenShared execution core for QA workflows. Apply a normalized findings list, validate changes, and produce a structured result that can be used by both local `/qa` and reactive `/pr-qa` flows.
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.