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...
rspack-perf
by web-infra-devUse when optimizing performance for user-specified files, features, compilation stages, Rust crates, JavaScript plugins, graph processing, parser work, chunking, code generation, or memory/CPU hot paths in Rspack.
rspack-perf-profiling
by web-infra-devRun Rspack performance profiling on Linux using perf (with DWARF call stacks), generate perf.data, and analyze hotspots. Use when you need CPU-level bottlenecks, kernel symbol resolution, or repeatable profiling for rspack build/bench cases. Includes optional samply import with per-CPU threads for visualization, but primary analysis is perf-based.
rspack-pgo
by web-infra-devRun Rspack's perf-guided optimization loop for `cases/all` and similar workloads: create an isolated worktree, build a profiling binding, benchmark with `RSPACK_BINDING`, collect and compare `perf` hotspots, implement small Rust changes, validate, commit, push, and trigger the Ecosystem Benchmark workflow after each pushed commit. Use this when the goal is iterative performance work, not just one-off profiling.
rspack-release-pr
by web-infra-devCreate the official Rspack release pull request for a stable or pre-release version bump. Use when the task is to prepare a formal release branch from a clean checkout, sync to the latest origin/main, run `./x version` with an optional `--pre alpha|beta|rc`, confirm the resulting JavaScript and Rust versions with the user, open the release PR, trigger Ecosystem CI, and report the PR plus workflow URLs.
rspack-sftrace
by web-infra-devUse sftrace, which is based on LLVM Xray instrumentation, to trace all Rust function calls. This can be used for performance analysis and troubleshooting.
create-draft-release-notes
by web-infra-devCreate or update draft GitHub releases for the current project's main GitHub repository, then organize GitHub-generated release notes into user-friendly sections without rewriting release note items. Use for preparing, formatting, categorizing, creating, or updating GitHub release notes or draft releases.
docs-en-improvement
by web-infra-devImprove English documentation under `website/docs/en` by rewriting unnatural translated sentences into clear, professional English while preserving meaning. Use when editing or polishing English docs.
write-e2e-cases
by web-infra-devUse when adding or updating Rsbuild end-to-end tests in `e2e/cases`, including new feature coverage, bug reproduction, and regression prevention.
upgrade-rspack
by web-infra-devUse when asked to upgrade `@rspack/core` in this repository to a specific version, run dependency installation and validation, then commit and create a pull request.
release-core
by web-infra-devUse when asked to release `@rsbuild/core` for a specific version.
sync-zh-en-docs
by web-infra-devSync uncommitted docs between `website/docs/zh` and `website/docs/en`. Use when authors update docs in one language and need to align the mirrored `.md`/`.mdx` file in the other language.
add-doc-anchor-ids
by web-infra-devAlign Rspress heading anchor IDs between English and Chinese docs. Use for MDX `\{#...}` anchors, shortened hashes, redundant anchors, or dead links.
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.