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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brain-find

by adithya0597
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Use this skill when the user wants to find something specific in their Second Brain — any search query, lookup, or "where did I write about X?" request. This covers searching across vault files, journal entries, concepts, action items, and the knowledge graph simultaneously, returning ranked results with graph-adjacent connections. Also use when the user wants to locate a note, recall when they discussed a topic, or check if they already captured something. Prefer this skill for any retrieval request, even vague ones. Distinguished from brain-trace (which follows a concept's evolution over time, not a point-in-time search), brain-resources (which catalogs the entire knowledge base rather than answering a specific query), and simple file reads (this searches across vault, DB, and knowledge graph together).

navigation main article SKILL.md
schedule Updated 3 months ago
adithya0597

brain-context-load

by adithya0597
star 0

Use this skill when the user wants to orient a fresh session with their Second Brain state — any request that signals "get up to speed on who I am and what I'm doing" without needing a daily note or morning briefing. This includes loading context at the start of a session, asking to be caught up, wanting to know current projects and priorities, or requesting a status snapshot. Reads identity files (ICOR, Values, Active Projects), queries SQLite for recent journal summaries, pending actions, attention flags, and active concepts. Distinguished from the morning review (which also loads context but additionally creates today's daily note and generates a morning briefing) — use context-load when you just need awareness, use brain-today when you're starting your day.

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schedule Updated 3 months ago
adithya0597

pr-review

by adithya0597
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Use this skill when reviewing code changes before committing to main — any request to review a diff, audit recent changes, or validate that modifications follow project conventions. This includes blast radius analysis (which modules import the changed code), security scanning (SQL injection in SQLite queries, credential exposure, unsafe eval), test coverage delta with pytest, breaking change detection (DB schema migrations, config key removals, ICOR hierarchy changes), and performance impact assessment (N+1 queries, unbounded loops, embedding recomputation). Produces a structured review with MUST FIX, SHOULD FIX, and SUGGESTIONS tiers plus a project-specific checklist. Distinguished from security-audit (which scans an entire directory for installation safety, not a commit diff) and from the code-simplifier agent (which refactors for clarity, not correctness or security).

navigation main article SKILL.md
schedule Updated 2 months ago
adithya0597

brain-sync-notion

by adithya0597
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Use this skill when the user wants to synchronize data between their local vault/SQLite and the Notion workspace — pushing local changes to Notion or pulling remote updates back. This covers any request to sync tags, tasks, projects, goals, journal entries, concepts, or people with Notion, whether full or selective. Also use when the user mentions data being out of date between systems, wants to push action items to Notion Tasks, or pull project status changes back locally. Distinguished from brain-refresh-dashboard (which only recalculates metrics and updates the cockpit, not raw entity data) and from one-off Notion API queries (this runs the full bidirectional sync pipeline with conflict resolution and state tracking).

navigation main article SKILL.md
schedule Updated 3 months ago
adithya0597

brain-emerge

by adithya0597
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Use this skill when the user wants to discover hidden patterns, implicit themes, or unnamed directions lurking across their notes — any request about "what patterns do you see," surfacing unconscious priorities, finding recurring questions, or synthesizing scattered observations into coherent insights. This includes detecting sentiment shifts, thematic clusters, converging interests, and conspicuous absences. Distinguished from brain-drift (which compares behavior against stated goals rather than discovering new patterns) and brain-graduate (which promotes already-identified recurring themes into concept notes rather than discovering them for the first time).

navigation main article SKILL.md
schedule Updated 3 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.