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.
Querying local SQLite index...
pr-green-sweep
by jscraikAutomate until-green PR review, CI, merge, and cleanup follow-through. Use when open project PRs need GitHub/gh, CodeRabbit, CircleCI, Context7, Snyk, autofix, heartbeat, and branch/worktree pruning.
talk-stack-humans-architect-ai-writes-code
by jscraikExplains Paul Stack's architecture-first AI workflow and helps create safe design artifacts: intent documents, architecture constraints, planner/reviewer loops, UAT criteria, and agent-output review gates. Use when the user asks about humans owning architecture while agents implement, why vibes do not scale, or applying the talk to team workflow design.
talk-wotherspoon-humans-vs-slop
by jscraikExplains Jack Wotherspoon's Humans vs Slop talk and helps create quality gates for AI-heavy software work: review-cost analysis, slop detection heuristics, durable-value metrics, and human-judgment checkpoints. Use when the user asks about AI-generated maintenance burden, review economics, or preserving taste in agentic development.
pnpm-manager
by jscraikRun, plan, and validate pnpm workspace operations. Use when a user needs pnpm monorepo installs, tests, builds, filters, changed-package selection, or publish routing.
talk-jones-odevo-ai-native-transformation
by jscraikUse when the user asks about Daniel Jones (Deejay) and Tomasz's talk "More software, faster — Odevo's AI Native transformation" — including questions about how Odevo (Sweden's third-largest private tech company, residential property management) rolled out agentic coding to its developers, the discovery → workshops → pilot → training → train-the-trainer playbook, prerequisites for adopting agentic coding (CI/CD, platform, tests, coding standards), liberating structures and TRIZ workshop techniques, context window management, the 94% AI adoption metric, the 8-years-to-3-weeks platform rewrite, shifting bottlenecks to product, the "everyone a builder" vision, or applying re-cinq's AI-native transformation approach to their own organisation.
talk-jourdan-pipelines-to-prompts
by jscraikAssists with questions about a practitioner panel talk titled 'From Pipelines to Prompts: Surviving the Shift to AI' featuring Stephane Jourdan, Simon (Saxo Bank), and Samantha. Use when a user asks about what panelists said, argued, or disagreed on regarding AI-native transformation, harness engineering, observability, developer cognitive load, feedback loops, reflector agents, or co-driving vs. self-driving analogies. Answers factual questions with verbatim transcript quotes, applies panelist frameworks to user situations, surfaces relevant panel insights during related discussions, and explains concepts like harness engineering, self-learning production agents, and explainability tooling.
talk-wilson-cq-stack-overflow-for-agents
by jscraikUse when the user asks about Peter Wilson and Davide Eynard's AI Native DevCon talk on cq, a Stack Overflow-like knowledge commons for agents, local/team/public knowledge sharing, and lessons from Mozilla.ai.
talk-ruiz-agents-on-canvas-tldraw
by jscraikUse when the user asks about Steve Ruiz's AI Native DevCon talk on tldraw, Make Real, annotations as prompt input, canvas workflows, tldraw computer, and agents collaborating on an infinite canvas.
talk-roberts-brownfield-ai-native
by jscraikUse when the user asks about Katie Roberts's talk "Stop Maintaining, Start Evolving: Applying AI-Native Engineering in Brownfield Codebases" (AI Native DevCon, June 2026) — including questions about brownfield vs greenfield AI engineering, the three methodologies (pseudo-greenfield, strangler fig pattern, branch by abstraction), the "code as a city" metaphor, using AI to map and modernize legacy codebases, planning skills and developer skills, the value-vs-complexity mirror exercise, avoiding AI agents going rogue on legacy code, the AG Grid upgrade case study, Nearform's "six months in eight weeks" pseudo-greenfield case study, or applying her brownfield AI-native approach to current legacy modernization work.
talk-moss-skills-team-workflow
by jscraikExplains James Moss's team-skills workflow and helps design skill governance: decomposition, ownership, versioning, eval scenarios, quality review, and lifecycle maintenance. Use when the user asks about moving from solo skill hacks to team workflow, avoiding skill sprawl, or treating skills like software.
og-image-creator
by jscraikGenerate route-aware Open Graph image workflows from existing web apps. Use this skill when route-specific social preview assets need refresh or creation.
fixing-metadata
by jscraikAudit, fix, and validate HTML metadata. Use when shipping pages that need titles, descriptions, canonical URLs, Open Graph tags, Twitter cards, favicons, JSON-LD, or robots directives.
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.