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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mironmax

kg-capture

by mironmax
star 1

Knowledge capture rules. Capture mid-conversation, not after — context is cached, so a write costs almost nothing now but saves full re-derivation next session. Good moments to capture (as things happen, not at task end): - Opened files with no component node → a brief node now saves a re-read later - Discovered how two things connect → an edge, while the insight is fresh - Understood why something works a certain way → a note on the existing node - 10+ min debugging resolved → root cause node before moving on - User expressed a preference, style, or constraint → user-level node - User corrected your approach → capture what was missed, not just the fix - Explained something non-obvious → node before it scrolls away - Approach agreed with user → capture the methodology, not just the decision - Architectural decision made → node with rationale in notes - Context window feels deep → a good moment to check for anything unrecorded When reading a file with no component node, consider creating

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

kg-core

by mironmax
star 1

Knowledge Graph — persistent memory, your twin across sessions. Treat it as primary context before reaching for any other tool. Session start: if kg_read hasn't been called yet, call it before any task work. kg_read(cwd="<project root>") Output has two sections — USER GRAPH and PROJECT GRAPH. On large graphs the result may start with <persisted-output> and show only a preview; the full output is saved to the file path shown — read it with the Read tool to get the complete picture including session_id. Announce "I have recalled KG Memories" once both sections have been read. Connection refused: the server auto-starts at session start (first run ~1 min) — retry after a few seconds. If tools stay offline, the user must run /mcp → plugin:knowledge-graph:kg → Reconnect. Manual fallback: `kg-memory start`. Before searching files, docs, or the web — check what's already known. The graph often has the answer, and reading from memory is faster than rediscovering. Writes during conversation are cheap (context is c

navigation main article SKILL.md
schedule Updated 14 days ago
mironmax

kg-extract

by mironmax
star 1

Map codebase architecture into the knowledge graph

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

kg-recall

by mironmax
star 1

Knowledge recall rules. Active every session, integrated with all task work. After kg_read, scan all node IDs and gists — anything that feels related to the current task is worth reading in full: kg_read(cwd, id). Lean toward reading more rather than less; a wrong guess costs one tool call, missing context costs the whole task. Gists are headlines (WHAT). Notes and touches hold rationale (WHY) — read those when a decision depends on understanding the reasoning, not just the fact. Three tiers — nodes shift as the graph grows: active → id + gist visible in kg_read archived → id only; edges visible as crumb trails orphaned → invisible in kg_read; reachable via kg_search Following crumbs: an edge pointing to an archived id is an invitation — kg_read(cwd, id) promotes it and surfaces any orphaned neighbors. No edges? Scan the archived list by name. kg_search reaches all tiers when you need to cast a wider net. Before stating an assumption — it's worth a quick kg_search first; the graph may already hav

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

kg-maintain

by mironmax
star 1

Knowledge graph maintenance. Tend the garden — regular, light care keeps it healthy. Not a separate task: woven into every session, every capture. GARDEN RHYTHM — three modes, applied as needed: Water (routine): after each task, glance at 2–3 recently-touched nodes. Are their gists still accurate? Any note worth adding? Prune (when dense): merge duplicate nodes, shorten verbose gists (→ notes), split oversized nodes, remove stale touches, delete edges to removed concepts. Fertilize (on use): when a node proves valuable, connect it to newly-discovered related nodes. One new edge makes a node far more durable. AFTER CAPTURE: when you save a node, immediately ask — - Do any adjacent nodes now need updating? - Is this a duplicate of something existing? Merge if so. - Does this node's gist still fit, or did context shift? REACTIVE TRIGGERS (act immediately, mid-conversation): Uncertainty (spinning wheels, deja vu, about to search)

navigation main article SKILL.md
schedule Updated 23 days ago
mironmax

kg-scout

by mironmax
star 1

Mine conversation history for patterns and insights worth preserving

navigation main article SKILL.md
schedule Updated 1 month 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.