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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grant-writing
by neuromechanistThis skill should be used when the user asks to "write a grant proposal", "draft specific aims", "write a research strategy", "create an NIH proposal", "create an NSF proposal", "write a significance section", "write an innovation section", "write an approach section", "draft a DP2 essay", "write an R01", "write an R21", "write a K99", "write an R03", "write a K08", "write a K23", "write an F31", "write an F32", "write a CAREER proposal", "write preliminary data", "write rigor and reproducibility", "draft potential problems and alternatives", "write a budget justification", "respond to reviewer comments", "write a resubmission introduction", "strengthen my specific aims", "format grant text", or mentions grant writing, proposal drafting, specific aims, research strategy sections, or any NIH/NSF mechanism.
opencite
by neuromechanistUse this skill for "search for papers", "find citations", "look up a DOI", "get BibTeX", "download PDF", "convert PDF to markdown", "find canonical papers", "convert identifiers", "batch download papers", "configure opencite", "literature review", "find related papers", "what papers cite this", "export references", "read this paper", or mentions of opencite, Semantic Scholar, OpenAlex, PubMed, academic literature search, citation management, or paper retrieval.
manuscript-formatting
by neuromechanistUse this skill for "format manuscript", "prepare for submission", "journal formatting", "LaTeX template", "submission checklist", "format references", "BibTeX", "author guidelines", "page limits", "format for Nature", "format for IEEE", or when the user wants to format a manuscript for journal submission.
lit-review
by neuromechanistUse this skill for "literature review workflow", "multi-phase lit review", "direction paper", "review paper protocol", "strand-based literature review", "citation-grounded review", "systematic lit review with paper cards", "build a lit review corpus", "lit review pipeline", "orchestrate a literature review", "research directions document", "write a literature review", "synthesize papers", "thematic review", "narrative review", "systematic review", "scoping review", "gap analysis", or when the user wants either a rigorous multi-phase citation-traceable lit review or a single-pass thematic synthesis for an Introduction/Background section.
document-processing
by neuromechanistThis skill should be used when the user says "process documents", "extract text from PDF", "OCR this document", "convert PDF to markdown", "extract emails from documents", "parse document", "document conversion", "batch OCR", "extract structured data from PDF", "read PDF", "extract tables from PDF", "convert Word document", "convert docx to markdown", or wants to extract, convert, or process documents and scanned images.
svg-primitives
by neuromechanistThis skill should be used when the user asks to "build an SVG schematic in Python", "programmatic SVG diagram", "auto-fit text in an SVG box", "Python flowchart with boxes and arrows", "SVG arrow that snaps to a box edge", "tangent-correct arrowhead on a curve", "SVG with controlled z-order layers", "auto-sized labeled box", "mm-precise SVG schematic", "SVG primitive layer", "draw flowchart from data in Python", "orthogonal SVG arrow routing", "Manhattan-style SVG routing", "bracket group of SVG elements", "leader line annotation", "label a group of SVG shapes", "strict mode svg-primitives canvas", "fail on text overflow in svg-primitives Canvas", "check text overflow on svg-primitives canvas", "Canvas.save validate=strict", or wants to author an SVG diagram in Python where text never overflows its container, arrows always touch their target box edge, paint order is deterministic, and connectors can be straight, cubic, or right-angled — and where validation findings are surfaced as soon as the SVG is saved. F
bids-conversion
by neuromechanistUse this skill for "convert to BIDS", "BIDS format", "create BIDS dataset", "BIDS sidecar", "participants.tsv", "events.tsv", "EEG BIDS", "EMG BIDS", "MEG BIDS", "fMRI BIDS", "BIDS validator", "channel locations", "electrode positions", "BIDS metadata", or when the user wants to convert neuroscience data to Brain Imaging Data Structure (BIDS) format or validate BIDS compliance.
experiment-design
by neuromechanistUse this skill for "design experiment", "create PsychoPy experiment", "stimulus presentation", "experiment protocol", "timing validation", "trial structure", "block design", "event-related design", "PsychoPy builder", "create stimuli", "LSL markers", "Lab Streaming Layer", "event markers", "trigger codes", or when the user wants to design or implement a neuroscience experiment.
manuscript-writing
by neuromechanistUse this skill for "write a paper", "draft manuscript", "write introduction", "write methods section", "write results", "write discussion", "write abstract", "structure a paper", "academic writing", "write for journal", or when the user wants to draft or revise sections of an academic manuscript.
humanizer
by neuromechanistUse this skill for "humanize this text", "remove AI tells", "make this less AI-sounding", "strip AI patterns", "de-AI my writing", "reduce AI-isms", "final natural-writing pass", "check for AI vocabulary", "em dash overuse", "rule of three overuse", "AI vocabulary", "signs of AI writing", or when polishing prose (papers, grants, lit reviews, abstracts, cover letters, responses to reviewers) to remove signs of AI-generated text while preserving meaning and discipline-appropriate conventions.
grant-review
by neuromechanistThis skill should be used when the user asks to "review a grant", "review my proposal", "score this grant", "evaluate my specific aims", "critique my research strategy", "review as an NIH reviewer", "review as an NSF panelist", "give me reviewer feedback", "check my grant proposal", "review my R01", "review my K99", "evaluate my CAREER proposal", "run a mock study section", "review my resubmission", "review this PDF", "check my proposal PDF", "analyze my grant layout", or mentions grant review, proposal critique, NIH scoring, NSF panel review, study section feedback, or proposal PDF review.
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.