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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kephalaia-skeleton-ocr
by kayna-of-lightRead a Kephalaia manuscript row by scanning skeleton templates across the connected ink. Defines the standing principles for character identification under the v2 OCR pipeline. Use when working on `temp/projects/kephalaia_ocr_v2/char_separation/skeleton_match_ocr.py` or any successor that scans skeleton templates against a row of ink. Keywords: kephalaia, skeleton, ocr, EMD, distortion, identity, perfect fit, impossible distortion, iota, brackets, coverage, scan, left-to-right.
manichaean-gdrive-management
by kayna-of-lightMirror or restore manichaean-analysis ignored/additional repo files with scripts/sync_artifacts_to_drive.py and Google Drive .git-data/manichaean-analysis.
manual-reviewer-api-route
by kayna-of-lightAdd or modify Next.js API route handlers in the Kephalaia Manual Reviewer (manual_reviewer/). Covers the route handler pattern, Zod request validation, SQLite repo access, dynamic params, and error handling. Use when adding new endpoints, fixing API bugs, or extending existing routes. Keywords: API, route, endpoint, handler, fetch, POST, GET, Zod, validation, SQLite, repo, server, Next.js route handler, manual reviewer.
manual-reviewer-feature
by kayna-of-lightAdd a complete feature to the Kephalaia Manual Reviewer (manual_reviewer/). Covers the full vertical slice: SQLite schema migration, repo functions, Zod schemas, API route handler, React Query hook, Zustand store (if needed), and UI component. Use when building a new capability end-to-end. Keywords: feature, vertical slice, new, add, scaffold, CRUD, migration, schema, store, hook, end-to-end, full stack, manual reviewer.
manual-reviewer-ui-component
by kayna-of-lightBuild UI components for the Kephalaia Manual Reviewer (manual_reviewer/). Covers MUI 9 + Tailwind 4 glass morphism theme, design tokens, dark mode, component composition, and accessibility. Use when creating or styling components, pages, dialogs, or layouts in manual_reviewer/. Keywords: UI, component, MUI, Material UI, Tailwind, glass, theme, dark mode, style, layout, dialog, page, card, Coptic, reviewer, manual reviewer.
literary-gdrive-management
by kayna-of-lightManage Google Drive sync for literary-compilation. Use when asked to mirror the Literary Compilation library to Drive, build styled PDFs, sync data markdown, force rebuild PDFs, dry-run Drive sync, authenticate Google Drive, inspect literary gdrive settings, or use scripts/mirror_library_to_drive.py.
thesis-writing
by kayna-of-lightWriting academic theses for the Divine Bricolage framework. Covers the complete iterative process from research gathering through outline creation, section-by-section writing, and Google Drive sync. Use when asked to write, create, or draft a thesis, paper, or academic document within this project's correspondential framework. Keywords: thesis, paper, write, draft, academic, correspondences, Ancient Word, framework.
coptic-ocr-reviewer
by kayna-of-lightUse when: reviewing unreviewed Coptic OCR scan-page markdown exports from the printed Kephalaia paper/edition, inspecting Final Reviewer Output text for suspicious OCR inconsistencies, and applying patterns learned only from manually corrected pages p010-p029 in temp/manual_reviewer_markdown_export/out.
kephalaia-editorial-fingerprint-mapping
by kayna-of-lightManual workflow for mapping German editorial sentences to cluster-array fingerprints in temp/editorial_sentences.json. Use when: Kephalaia editorial fingerprints, German notes, LLM witness lines, Manual Reviewer editorial layer, cluster arrays, abgerieben, zerstört, leer, unlesbar, geringe Spuren.
kephalaia-glyph-skeleton
by kayna-of-lightDerive a clean single-line pen-stroke skeleton for one Coptic character of the Kephalaia manuscript. The workflow goes one character at a time. For each target glyph, draw a wide net of candidates from the v2 body crops, render an indexed atlas, visually triage at high zoom, hand-pick 20 verified specimens spread across the manuscript, then synthesize one skeleton by pixel-vote over normalized cutouts. Outputs go to `temp/projects/kephalaia_ocr_v2/glyph_seed_library/per_char/<slug>/` and the family JSON is written to `manual_template_line_profiles/` so that `glyph_seed_workflow.py character-sheet --include-special` picks it up. Use when asked to derive a skeleton for a Coptic character, build a glyph seed family, or regenerate the character review sheet with a new character. Keywords: kephalaia, skeleton, glyph, gangia, ϫ, ⲉ, ⲱ, ⲧ, cutout, atlas, synthesize, single-line trace, per-character workflow.
kephalaia-page-audit
by kayna-of-lightPer-page visual audit of Kephalaia v2 page JSON files against the manuscript image and Gardner translation. Use when asked to audit, fix, verify, or correct page JSON files in output/projects/kephalaia_v2/pages/. Keywords: kephalaia, page audit, leiden, brackets, lacuna, restoration, uncertain, leer, scriptio continua, Polotsky, Böhlig.
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.