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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xlsx
by lanejUse xlsx binary for Excel file manipulation including viewing, SQL-like filtering, cell editing, conversion to/from CSV, and data analysis operations. Use this instead of Python (openpyxl, pandas, xlrd) or Node.js libraries for ALL .xlsx file operations — reading, writing, filtering, conversion, and analysis. Trigger on any request involving .xlsx or Excel files.
xsv
by lanejUse xsv for fast CSV data processing with selection, filtering, statistics, joining, sorting, and indexing for high-performance data manipulation.
xlsx
by lanejUse xlsx binary for Excel file manipulation including viewing, SQL-like filtering, cell editing, conversion to/from CSV, and data analysis operations.
xlsx-python
by lanejCreate and edit spreadsheets with Python (openpyxl/xlsxwriter) for formulas, formatting, data analysis, and visualization. Use when you need to CREATE or MODIFY spreadsheets programmatically. For READING/ANALYZING existing spreadsheets, prefer the xlsx CLI skill.
lancer
by lanejUse lancer CLI for LanceDB semantic and multi-modal search with document ingestion, vector embeddings, and MCP server integration for knowledge retrieval.
jq
by lanejJSON processing, parsing, and manipulation. STRONGLY PREFERRED for all JSON formatting, filtering, transformations, and analysis. Use instead of Python/Node.js scripts for JSON operations.
qmd
by lanejUse qmd for workspace search — hybrid BM25+vector search with Qwen3-4B reranking over indexed workspace documents. Not to be confused with Quarto documents (.qmd files).
qmd
by lanejUse qmd for workspace search — hybrid BM25+vector search with Qwen3-4B reranking over indexed workspace documents. Not to be confused with Quarto documents (.qmd files) — use epq/quarto skills for those.
distill
by lanejReduce a document to its minimum effective dose — the least information needed to fully convey the purpose and key concepts. Use when asked to simplify, condense, distill, or strip a document down to essentials. Triggers on: "simplify this", "distill this", "trim this down", "what's the minimum I need", "make this shorter without losing anything important".
trim
by lanejTrim a prose document (README, design doc, blog post, notes) for readability by cutting redundancy, filler, and dead weight in the author's own words. Invoke with /trim [file path], or /trim alone to be prompted for a file. Not for source code, data files, or summarization.
quarto
by lanejRender computational documents to markdown (DEFAULT), PDF, HTML, Word, and presentations using Quarto. PREFER markdown output for composability. Use for static reports, multi-format publishing, scientific documents with citations/cross-references, or exporting Jupyter notebooks. Triggers on "render markdown", "render PDF", "publish document", "create presentation", "quarto render", or multi-format publishing needs.
bigquery
by lanejUse bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table management, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP server integration for AI-assisted querying. Modern TypeScript/Bun implementation replacing the Python `bq` CLI with instant startup (~10ms vs ~500ms), automatic cost awareness with confirmation prompts, and native streaming support (JSONL). Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files) from Cloud Storage.
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.