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...
source-evaluation
by trevormentis-specScore sources for reliability and credibility using the NATO Admiralty Code (A1–F6) and calibrate confidence using Sherman Kent probability bands. Use whenever Trevor cites an OSINT source, evaluates conflicting reports, or attaches a confidence band to a key judgment. Triggers on any request involving "how reliable is", "rate this source", "what's our confidence", "probability", or "Admiralty code".
trevor-web-collection
by trevormentis-specUse for any collection task that involves extracting structured data from a website. Always prefer programmatic API access (openweb spec → reverse-engineered API → window globals → DOM scrape, in that order). Trigger on: "collect from <site>", "pull data from <url>", "scrape", "monitor <web platform>", "get the latest <articles|posts|data> from <site>", "fetch <resource> from <site>", "load <site> <content>", "search <site> for <query>", "pull <site>", "get <site> <section>", "extract <data> from <site>".
intelligence-ingestion
by trevormentis-specAnalyze and evaluate URLs, links, articles, tweets, and external info sources for strategic value. NOT a summarizer — this skill classifies, scores importance, maps to architecture, stores structured notes in Obsidian, updates the Strategic Landscape, and auto-generates draft Skills when new capabilities are detected (Auto-Skill Synthesis). Use when: user shares a link (x.com, github.com, arxiv.org, any URL), pastes article text, says "analyze this", "evaluate this", "what do you think about this", or forwards content. Do NOT use for: simple summarization requests (use summarize skill instead).
mapbox-data-visualization-patterns
by trevormentis-specPatterns for visualizing data on maps including choropleth maps, heat maps, 3D visualizations, data-driven styling, and animated data. Covers layer types, color scales, and performance optimization.
mapbox-geospatial-operations
by trevormentis-specExpert guidance on choosing the right geospatial tool based on problem type, accuracy requirements, and performance needs
pdf-report
by trevormentis-specGenerate clean A4 PDF reports from structured JSON using Jinja2 and WeasyPrint. Use when the user needs a formatted PDF document — analytical summary, data report, or chart-based export — from workspace data.
daily-intel-brief
by trevormentis-specProduce Trevor's Daily Intelligence Brief — a dated, structured intelligence product with ten regional sections (Europe, North America, Central America & Caribbean, South America, Africa, Middle East, Central Asia, South East Asia, Oceania, Prediction Markets), each carrying calibrated key judgments and prediction bands. Trigger this skill on "daily brief", "morning brief", "today's brief", "INTSUM", "daily intel", "Trevor brief", "run the brief", "kick off today's product", "where are we today", or any standing-product request that implies the daily cadence. Composes existing analyst skills (sat-toolkit, source-evaluation, indicators-and-warnings, bluf-report, geospatial-osint, chartgen, mermaid, pdf-report) and the analyst/ scaffold; routes analysis to deepseek-v4-pro per ORCHESTRATION.md escalation criteria. Do not use this skill for ad-hoc single-region deep dives, single-incident analysis, or non-security topics — those should run through the standard analytic-workflow playbook.
qgis
by trevormentis-specRun QGIS geospatial processing with qgis_process for repeatable vector/raster workflows (reproject, clip, dissolve, buffer, merge, raster warping). Use when the user asks for GIS/QGIS automation, coordinate system conversion, geodata transformation, or batch map data processing. 中文触发：QGIS、地理处理、坐标转换、矢量裁剪、栅格重投影。
a2a
by trevormentis-specGoogle A2A (Agent-to-Agent) Protocol — lets Trevor discover, communicate with, and be discovered by other AI agents. JSON-RPC 2.0 over HTTP. Linux Foundation project, Apache 2.0.
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.