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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latex-document-skill
by ndpvt-webUniversal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detect
prompt-improver
by ndpvt-webOptimize prompts for better AI responses. Use when user asks to improve a prompt, refine a prompt, make a prompt better, optimize prompting, "Aristotelian", "first principles", or "proof-based" , review their prompt, or says "/improve-prompt". Transforms vague requests into clear, specific, actionable, logic-based prompts.
humans-welcome-observe-first-look
by ndpvt-webAnalyze AI agent social network activity using topic taxonomy classification and multi-level toxicity scoring. Detects content flooding, topic concentration, temporal risk patterns, and manipulative rhetoric in agent-generated discourse. Use when: 'classify agent posts by topic and toxicity', 'detect bot flooding patterns', 'analyze toxicity distribution in a social platform', 'monitor AI agent community health', 'find manipulative rhetoric in automated content', 'audit agent discourse for risk patterns'.
synthagent-multi-agent-framework-realistic
by ndpvt-webBuild multi-agent pipelines that generate realistic synthetic patient profiles by integrating epidemiological data, medical claims, literature evidence, and personality models. Use when asked to 'simulate patients', 'generate synthetic medical data', 'build a patient simulation pipeline', 'create virtual patient cohorts', 'model disease progression with agents', or 'synthesize clinical profiles'.
evaluating-achieving-controllable-code
by ndpvt-webInstruction-guided code completion that follows user constraints on algorithm choice, data structures, control flow, and code scope. Use when: 'complete this function using a deque-based BFS', 'finish this code with exactly 3 lines', 'implement the sort using quicksort not mergesort', 'complete using recursion instead of iteration', 'fill in this block with a single for loop', 'generate the rest using dynamic programming'.
agentcpm-report-interleaving-drafting-deepening
by ndpvt-webGenerate deep research reports by interleaving evidence-based drafting with reasoning-driven deepening. Uses the WARP (Writing As Reasoning Policy) framework from AgentCPM-Report to dynamically evolve outlines during writing instead of rigidly following a static plan. Trigger phrases: "deep research report", "write a comprehensive analysis", "investigate and write up", "research report on", "deep dive report", "analyze this topic thoroughly and produce a report"
evocodebench-human-performance-benchmark-self-evol
by ndpvt-webSelf-evolving code generation with iterative reflection and revision. Applies a feedback-driven loop where code is submitted, judged, analyzed for failures, and rewritten up to 3 times — tracking correctness, runtime, memory, and algorithmic improvement at each iteration. Use when: 'solve this coding problem and optimize it', 'iteratively improve this solution', 'refine my code until it passes all tests', 'benchmark my solution against human performance', 'reduce the time complexity of this code', 'fix and re-attempt this failing solution'.
prism-xr-empowering-privacy-aware-xr
by ndpvt-webBuild privacy-aware pipelines that filter sensitive content from visual frames before sending to cloud AI models, using edge preprocessing with object detection (YOLO), text-based scene description, selective cropping, and structured MLLM interaction. Triggers: 'privacy-aware XR pipeline', 'filter sensitive data from camera frames', 'edge preprocessing before cloud AI', 'PRISM-XR privacy pipeline', 'sanitize visual input for LLM', 'multi-user XR collaboration with privacy'
automating-computational-reproducibility-social
by ndpvt-webDiagnose and repair failing computational research code to restore reproducibility. Uses an agent-based iterative workflow: inspect files, identify failures (missing packages, broken paths, version conflicts, missing logic), apply targeted fixes, and rerun in isolated environments. Trigger phrases: 'reproduce this analysis', 'fix this R script', 'make this code reproducible', 'debug this research pipeline', 'repair computational workflow', 'rerun this study'
pcbschemagen-constraint-guided-schematic-design
by ndpvt-webGenerate PCB schematics from natural language using constraint-guided LLM code generation with knowledge-graph verification. Use when the user says 'generate a PCB schematic', 'design a circuit board', 'create a KiCad schematic from description', 'convert circuit requirements to netlist', 'automate schematic design', or 'generate SKiDL code for a circuit'.
following-dragons-code-review-guided
by ndpvt-webExtract security-relevant signals from code review comments and translate them into fuzzer-guiding annotations using the EyeQ pipeline. Use when the user says 'guide fuzzing from code reviews', 'find dragons in review comments', 'annotate code for fuzzing', 'review-guided fuzzing', 'extract security signals from PRs', or 'instrument code for AFL++ from review discussions'.
malicious-repurposing-open-science
by ndpvt-webDefensive dual-use risk assessment for open science artifacts. Evaluates research papers, datasets, methods, and tools for repurposing vulnerabilities using a structured pipeline based on Hashemi et al. (2026). Produces risk reports with harmfulness, feasibility-of-misuse, and technical-soundness scores. Trigger phrases: "assess dual-use risk of this paper", "evaluate artifact repurposing risk", "run dual-use risk audit on this dataset", "check this tool for misuse potential", "security review of open science artifacts", "red-team this research for repurposing vulnerabilities"
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.