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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datadog-entity-generator
by zircoteGenerate exhaustively complete and accurate Datadog Software Catalog entity YAML files (v3 schema) by examining project source code and interviewing engineers. Use when engineers need to create or update entity.datadog.yaml files for services, datastores, queues, APIs, or systems. Triggers include: "create entity yaml", "generate service catalog entry", "document this project in datadog", "create service definition", "add to software catalog", or any request involving Datadog entity/service documentation. Supports all v3 entity kinds: service, datastore, queue, api, system. Fetches existing Datadog data via API. Validates against official JSON schema. Merges with existing definitions. Outputs to .datadog/ directory.
chrome-devtools
by zircoteBrowser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
python-project-skel
by zircoteGenerate production-ready Python project skeletons with Astral UV package manager, Hatchling build backend with dynamic versioning, and modern tooling (ruff, mypy, pytest, bandit). Use when creating new Python projects, initializing Python packages, setting up src-layout projects, scaffolding Python libraries, or starting a new Python application. Supports Python 3.14+ by default with configurable version. Output to current directory, specified path, or tarball.
root-cause-tracing
by zircoteSystematically trace bugs backward through call stack to find original trigger
blackboard
by zircoteCross-session handoff, persistent context via blackboard, and agent coordination patterns
custodian
by zircoteMemory system custodian for health checks, validation, and maintenance. Trigger phrases: "check memory health", "validate memories", "fix broken links", "update decay", "relocate memories", "audit memories", "memory maintenance", "custodian", "memory health"
qmd-reindex
by zircoteRe-index mnemonic memories for qmd semantic search. Run after capturing new memories or bulk imports.
qmd-setup
by zircoteSet up @tobilu/qmd semantic search for mnemonic memories. Registers collections, builds indexes, and generates embeddings. Run this once per machine.
rlm-pattern
by zircoteProcess files exceeding context limits using the RLM (Recursive Language Model) pattern with agent teams. Use when you need to process large files, analyze documents exceeding context, apply RLM chunking, chunk and analyze large content, or handle long context documents.
migrate
by zircoteMigrate legacy sigint configuration (sigint.local.md or .sigint.config.json v1.0) to sigint.config.json v2.0 with per-topic support and CONTEXT.md generation. Safe, idempotent, supports --dry-run preview and --global flag. Handles both YAML frontmatter and markdown section formats.
api-documentation
by zircotePatterns and templates for creating comprehensive API documentation using OpenAPI/Swagger, AsyncAPI, and manual documentation approaches. Use this skill whenever the user wants to document REST endpoints, generate or improve an OpenAPI spec, create Swagger docs, document WebSocket/event-driven APIs with AsyncAPI, review API documentation completeness, add request/response examples, document authentication schemes, set up API documentation portals (Swagger UI, ReDoc, Stoplight), or convert between API doc formats. Also applies when discussing endpoint parameters, response codes, schema definitions, pagination patterns, error response formats, or code-first vs design-first API documentation. If the user mentions "API docs", "OpenAPI", "Swagger", "AsyncAPI", "endpoint documentation", "API reference", or "API spec", this skill likely applies.
python-project-skel
by zircoteGenerate production-ready Python project skeletons with Astral UV package manager, Hatchling build backend with dynamic versioning, bump-my-version for semantic version management, and modern tooling (ruff, mypy, pytest, bandit). Use when creating new Python projects, initializing Python packages, setting up src-layout projects, scaffolding Python libraries, or starting a new Python application. Supports Python 3.14+ by default with configurable version. Output to current directory, specified path, or tarball.
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.