381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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command and control center specialists
Showing 12 of 141 skills
Demerzels-lab

maritime-watch

by Demerzels-lab
star 9

A skill for monitoring the status and security of the Chornomorsk port.

navigation main article SKILL.md
schedule Updated 1 month ago
nimbusxr

cot-comms

by nimbusxr
star 5

Send CoT events and chat messages to other TAK users and groups

navigation main article SKILL.md
schedule Updated 2 months ago
nimbusxr

grg-builder

by nimbusxr
star 5

Place Gridded Reference Graphics (GRGs) on the map

navigation main article SKILL.md
schedule Updated 2 months ago
nimbusxr

mil-symbology

by nimbusxr
star 5

MIL-STD-2525 symbol lookup, placement, and SIDC decoding

navigation main article SKILL.md
schedule Updated 2 months ago
nimbusxr

spot-map

by nimbusxr
star 5

Create spot report markers with military categorization

navigation main article SKILL.md
schedule Updated 2 months ago
nimbusxr

video

by nimbusxr
star 5

Discover and manage video ISR feeds

navigation main article SKILL.md
schedule Updated 2 months ago
caishengold

military-doc-writer

by caishengold
star 1

当需要撰写军事文档相关专业文案、行业指南、科普文章时使用。触发场景:军事文档/操作规程。当用户提到"军事文档"、"军事文档"、"操作规程"、"military"、"doc"时应触发此技能。

navigation main article SKILL.md
schedule Updated 4 months ago
sunjong5108

wargame-query

by sunjong5108
star 1

Tactical map query skill for accessing war game state information. Use when needing information about unit positions, friendly forces, hostile forces, or tactical situation from the interactive map.

navigation main article SKILL.md
schedule Updated 2 months ago
ryansketch01

admiralty-grading

by ryansketch01
star 0

Use when grading any source-claim pair for intelligence findings — assessing source reliability (A-F) and information credibility (1-6) per NATO AJP-2.1. Invoke before promoting raw signal to findings, before setting WEP ceilings, before assigning inclusion eligibility, when encountering an unfamiliar source, when first-party Splunk telemetry contradicts an external source, or any time a digraph rating is needed. Also invoke when recalibrating an existing finding after new corroboration arrives or when a source's track record changes.

navigation main article SKILL.md
schedule Updated 2 months ago
erichokh65

multi-document-reconciliation-gate

by erichokh65
star 0

Use when performing the final cross-document quality gate to verify schedule, risk, cost, charter, dashboards, monthly report, and stakeholder presentation are mutually consistent before handoff.

navigation main article SKILL.md
schedule Updated 2 months ago
HamishT26

journey-absorption-v9-operations

by HamishT26
star 0

Operate the Journey Absorption V9 pack with explicit v9 council, live-sync, and synthetic-mesh boundaries.

navigation main article SKILL.md
schedule Updated 3 months ago
laudoluxDev

militaryspousetasksai

by laudoluxDev
star 0

Access 166+ AI-powered skills for military spouses, veterans, and military families. Use when: user asks about PCS moves, TRICARE coverage, BAH entitlements, military spouse employment, education benefits, power of attorney, family readiness, or any military family administration task.

navigation main article SKILL.md
schedule Updated 2 months ago
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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.