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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framework-compliance-review
by GravelawPerform standards-guided and adversarial engineering reviews for software, data, ML, AI, hardware-integrated, and system designs. Use when Codex should assess architecture, implementation, test plans, readiness, or compliance-style evidence against ISO/IEC/IEEE 12207, ISO/IEC/IEEE 42010, ISO/IEC 25010, SEI ATAM, NIST SSDF, OWASP ASVS/SAMM, NIST AI RMF, NASA/JPL Power of Ten, or role-based architecture review gates.
plan-work
by GravelawPlan coding, ML, data analysis, data science, AI engineering, and system design tasks before implementation using a governed review framework. Use when a task is broad, risky, cross-file, experimental, data-dependent, security-sensitive, architecture-relevant, or likely to need staged execution and validation.
post-execution-review
by GravelawReview work after execution for coding, ML, analytics, data science, AI engineering, and system design tasks using framework gate evidence. Use after tests, scripts, notebooks, package installs, model runs, architecture reviews, or compliance-style checks to inspect results, diffs, artifacts, residual risks, and next actions.
review-architecture
by GravelawReview planned or implemented architecture for software engineering, ML, analytics, data science, AI systems, hardware-integrated systems, and enterprise/system designs using ISO/IEC/IEEE 42010, SEI ATAM, ISO/IEC 25010, NIST SSDF, OWASP ASVS/SAMM, NIST AI RMF, and NASA/JPL Power of Ten. Use before implementation, before large refactors, before publishing, or when assessing maintainability, testability, reproducibility, security, safety, and compliance-style readiness.
analytic-question-framing
by GravelawRefine vague data science requests into precise, answerable analytic questions. Use when a user asks what analysis to run, provides a broad business or research goal, needs to classify a question as descriptive, exploratory, inferential, predictive, causal, or mechanistic, or needs acceptance criteria before data cleaning, EDA, modeling, or reporting.
data-analysis-workflow
by GravelawOrchestrate practical data science work from vague request to verified analysis, report, or handoff. Use when Codex needs to plan or execute an end-to-end data analysis, decide which data science sub-skill to use, turn a stakeholder question into an analysis workflow, review an analysis for completeness, or coordinate EDA, modeling, interpretation, communication, and reproducibility.
domain-problem-interviewer-researcher
by GravelawInterview stakeholders and research domain context before data science work begins. Use when a data science request starts from a business need, vague query, opportunity, operational pain point, domain problem, product question, policy question, or research question and Codex needs to ask structured discovery questions, identify the business/domain/end goal, perform web research from authoritative sources when internet access is available and not forbidden, and produce a domain briefing plus next-stage data science plan.
modeling-strategy-review
by GravelawChoose, review, or debug data science modeling strategy. Use when Codex needs to decide between inference, prediction, causal, clustering, topic modeling, graph, time-series, or baseline approaches; review model assumptions; prevent leakage and overfitting; design train/validation/test splits; interpret coefficients or metrics; or calibrate claims against evidence.
reproducible-analysis-reporting
by GravelawTurn data science notebooks, scripts, and results into reproducible reports or handoffs. Use when Codex needs to rerun an analysis, structure notebooks, produce written findings, create reproducibility instructions, document data lineage, set seeds, compare generated outputs, prepare stakeholder reports, or communicate results with caveats and clear figures.
julia-coding
by GravelawImplement, review, refactor, or explain Julia code; set up Julia projects and packages; choose Julia libraries; and follow idiomatic Julia package, module, testing, and Pkg workflows. Use when the user wants help writing Julia, structuring a Julia package, managing Project.toml and Manifest.toml environments, selecting packages, or reviewing Julia-specific code.
julia-data-science
by GravelawBuild, review, or debug Julia data analysis, data science, ML engineering, notebooks, feature pipelines, evaluation workflows, visualizations, and reproducible experiment code. Use when the task involves DataFrames, CSV, Arrow, Tables, MLJ, Flux, GLM, StatsBase, Plots, Pluto, IJulia, or Julia data/ML packages.
julia-debugging
by GravelawDiagnose, reproduce, and fix Julia errors, stack traces, package-loading failures, logging issues, type-instability problems, test failures, Pkg/environment issues, notebook failures, model failures, and performance regressions.
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.