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...
bprr
by wilsonfreitasBulk review all unreviewed pull requests for the awesome-quant curated list. Review all open PRs without the "reviewed" label in one session, then present a summary table for user selection before merging. Use this skill when asked to "bulk review", "review all prs", "bprr", or "review unreviewed".
sprr
by wilsonfreitasSingle PR reviewer. Review and validate pull requests that add new library entries to the awesome-quant README.md. Use this skill whenever the user asks to review PRs, check contributions, validate submissions, triage pull requests, or mentions anything about incoming entries or additions to the curated list. Also triggers for "review pr", "check prs", "merge contributions", "sprr", or any PR-related workflow in this repo.
update-pypi-dates
by wilsonfreitasCheck PyPI projects for last updated dates and update README.md entries. Use this skill to keep PyPI project information current with latest release dates from PyPI. Run periodically to maintain up-to-date metadata on Python packages listed in awesome-quant.
brasa-db-explorer
by wilsonfreitasConnect to BrasaDB's DuckDB database and execute SQL queries to explore Brazilian financial market data. Use this skill whenever the user asks to query, explore, analyze, or study data in the brasa database, asks about available datasets or tables, wants to run SQL queries against financial data, wants to create views or combine datasets for a specific application, or mentions DuckDB, BrasaDB, or SQL in the context of this project. Also trigger when the user asks questions that could be answered by querying the data — e.g., "what stocks are in IBOV?", "show me PETR4 prices", "what's the DI1 curve?", "help me create a view with these datasets", "I have datasets A and B, combine them for X".
brasa-template-writer
by wilsonfreitasWrite new brasa YAML templates from scratch or migrate legacy templates (with reader.function and handler-based fields) to the modern pipeline-based format. Use when the user asks to create a new template, write a template, migrate a legacy template, convert an old template, or work with YAML template definitions. Also trigger when the user mentions template creation, template migration, or refers to legacy/old templates.
linear-brasa-brainstorm
by wilsonfreitasBrainstorm and save a design spec into a Linear issue for the brasa project. Wraps superpowers:brainstorming but persists the final design as a `## Design` section inside the target issue's description instead of a `.md` file. Use whenever the user says "refine WIL-X", "brainstorm WIL-X", "design WIL-X", or otherwise wants to turn a brasa Linear issue into a reviewed design spec.
linear-brasa-executor
by wilsonfreitasExecute a previously-planned Linear issue for the brasa project end-to-end: read the plan from the issue, work through each step/phase/task faithfully, maintain an "Execution log" comment on the issue, check off items in the plan's checklist as they complete, and only mark the issue Done after all tests, ruff, and pre-commit pass. Use this skill whenever the user says things like "execute WIL-X", "run the plan for issue X", "implement WIL-X", "carry out this issue", "let's do WIL-X", or otherwise wants to turn a planned Linear issue into actual code changes — even if they don't say the word "execute".
linear-brasa-plan
by wilsonfreitasProduce an implementation plan from a brasa Linear issue's existing `## Design` section and save it as `## Implementation Plan` inside the same issue's description. Wraps superpowers:writing-plans but persists to Linear instead of a `.md` file. Use whenever the user says "plan WIL-X", "write the plan for WIL-X", or otherwise wants to turn an already-designed issue into an actionable plan.
linear-brasa
by wilsonfreitasManage the Linear workflow for the brasa project: saving ideas as issues, and refining/planning issues using EnterPlanMode and updating them in Linear. Use this skill whenever the user mentions Linear, saving an idea, refining a ticket, or planning a feature — even if they don't explicitly say "Linear" but the context is clearly about brasa development tasks.
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.