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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packmind-onboard
by PackmindHubComplete automated onboarding: analyzes codebase, creates package, and generates standards & commands via CLI. Automatic package creation when none exist, user selection when packages are available.
michel-ui-demo-recorder
by PackmindHubRecord polished UI demo videos and screenshots of a running web app using Playwright MCP — for client deliverables, release notes, feature walkthroughs, or bug repros. Produces an HD WebM video with chapter markers, a mandatory animated cursor overlay, and a mandatory subtitle bar that narrates each step (positioned deliberately so it never masks the UI being demonstrated), plus full-page screenshots at each step. Use this whenever the user asks to "record a demo", "create a screencast", "make a UI walkthrough video", "document this feature with video", "show the client how X works", "capture screenshots of the app", or anything similar — even when the user only says "make a video" or "take screenshots" in the context of a running frontend. Also use it when the user wants to demonstrate a workflow, generate marketing-quality footage of an app, or produce repeatable visual documentation.
michel-run-local-dev-stack
by PackmindHubThe canonical recipe for starting, checking, and stopping the Packmind local dev stack with Docker Compose — the single source of truth other skills and the Michel agent defer to. Covers bringing the full stack (PostgreSQL, Redis, NestJS API, React/Vite frontend on :4200, MCP server, nginx) up in the background, the init services (dependency install + TypeORM migrations) you must wait on, the critical host-port trap that the API on container port 3000 is NOT exposed to the host and must be reached via the frontend Vite proxy at localhost:4200/api/v0, confirming the API and frontend are actually serving before you depend on them, the persistent-volume gotcha that leaves stale Postgres schema and node_modules behind between runs, building the CLI, and tearing everything down so no container is left blocking the run. Use this whenever you need Packmind running locally — to verify a change, record a UI or CLI demo, hit the API, seed data, or reproduce a bug — and whenever you are about to start or stop `docker co
michel-create-pr-with-screenshots
by PackmindHubBest practices for creating GitHub pull requests that include inline images — CLI terminal screenshots (from cli-demo-recorder), UI screenshots/videos (from ui-demo-recorder), or any other visual artifact. Use this skill whenever opening or updating a PR that has visual artifacts to embed, or when images aren't rendering in a PR description. Also use it when asked "how do I add screenshots to a PR", "why isn't my image showing", or "embed a demo in the PR".
michel-cli-demo-recorder
by PackmindHubProduce proof-of-execution demos of the Packmind CLI (`packmind-cli`) as terminal-styled images (colors and formatting preserved exactly), for embedding in a GitHub PR. Renders a crisp master SVG and rasterizes it to a PNG — the PNG is what you embed, because GitHub does not render SVG in PR/issue bodies. Use this whenever a dev task touches the CLI — new command, changed output, new flag, bug fix in terminal rendering — and the PR would benefit from showing the tool actually running. Trigger it when the user says "record a CLI demo", "show the command output", "add a terminal screenshot to the PR", "prove the CLI works", "capture the CLI", "demo the command", or whenever you finish CLI work and are about to open or update a PR. Default to running this for any PR whose diff includes CLI source, even if the user didn't explicitly ask for a screenshot — a CLI PR without a visual of the output is an incomplete deliverable.
packmind-onboard
by PackmindHubComplete automated onboarding: analyzes codebase, creates package, and generates standards & commands via CLI. Automatic package creation when none exist, user selection when packages are available.
packmind-create-standard
by PackmindHubGuide for creating coding standards via the Packmind CLI. This skill should be used when users want to create a new coding standard (or add rules to an existing standard) that captures team conventions, best practices, or coding guidelines for distribution to GitLab Duo.
packmind-create-skill
by PackmindHubGuide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends GitLab Duo's capabilities with specialized knowledge, workflows, or tool integrations.
packmind-create-command
by PackmindHubGuide for creating reusable commands via the Packmind CLI. This skill should be used when users want to create a new command that captures multi-step workflows, recipes, or task automation for distribution to GitLab Duo.
working-with-pm-design-kit
by PackmindHubThis skill provides guidance for using the Packmind UI component library (@packmind/ui). It should be used when building or modifying frontend UI with PM-prefixed components, working with Chakra UI in the Packmind codebase, or when questions arise about available components, theming, or layout patterns. Triggers on mentions of PM components, @packmind/ui, Chakra UI usage, design kit, or frontend component implementation.
working-with-playground-app
by PackmindHubThis skill provides guidance for building UI/UX prototypes in the Packmind playground app. It should be used when creating a new prototype, iterating on an existing prototype, or working with files in apps/playground/. Triggers on mentions of "playground", "prototype", or direct work within the apps/playground/ directory.
packmind-update-playbook
by PackmindHubUse when updating, adding, fixing, changing, or deprecating Packmind playbook artifacts (standards, commands, skills). Triggers on explicit phrases like "update packmind standard", "add a packmind skill", "fix packmind command", "change packmind playbook", "deprecate a standard". Also triggers — even without an explicit request — whenever the conversation reveals an opportunity: a new coding convention was just agreed on, a recurring pattern emerged, a workflow changed, a rule was found outdated, or the user says things like "we always do X", "let us remember to Y", "that is the pattern we use". If there is any chance the conversation established a convention or exposed a gap, invoke this skill proactively. This skill defines a mandatory workflow: do NOT edit artifact files directly — follow all phases regardless of change size.
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.