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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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app-implement-feature
by tddworksGuide for implementing features in baguette — a Swift CLI + WebSocket server that drives iOS simulators via private SimulatorKit. Use this skill when: (1) Adding a new gesture, button, keyboard surface, stream format, or device-chrome behaviour (anything that lands across Domain / Infrastructure / App + Resources/Web). (2) Extending an existing wire-protocol envelope, CLI subcommand, or HTTP route. (3) User asks "add feature X to baguette", "implement <gesture>", "wire <new verb> through serve / input / CLI", or similar. (4) Touching the iOS-26 SimulatorKit / IndigoHID surface — those edits MUST go through this skill's Architecture phase before code lands. Avoid using this skill for refactors that don't add a new feature (keep those TDD-driven without the architecture-approval gate).
baguette
by tddworksDrive iOS simulators programmatically via the `baguette` CLI — taps, swipes, multi-finger gestures, hardware buttons, frame capture, all without opening Xcode. Use this skill when: (1) The agent needs to interact with a booted iOS simulator from a script (tap a coordinate, swipe between points, send Home / Lock / Volume / Action / Power, type ASCII text via the keyboard) (2) Building a smoke test, demo recording, or UI flow that drives a simulator end-to-end (3) Pairing iOS development with Claude Code, where the agent needs to verify on-screen state after a code change (4) User asks "tap the simulator from a script", "automate iPhone gestures", "control iOS sim programmatically", "drive simulator without Xcode" (5) User mentions `baguette`, `baguette input`, `baguette tap`, `baguette serve`, or `baguette stream` by name (6) An iOS smoke-test / fixture / SwiftUI verification needs to actually *touch* the running app, not just inspect static code Avoid using this skill for plain "op
add-provider
by tddworksGuide for adding new AI providers to ClaudeBar using TDD patterns. Use this skill when: (1) Adding a new AI assistant provider (like Antigravity, Cursor, etc.) (2) Creating a usage probe for a CLI tool or local API (3) Following TDD to implement provider integration (4) User asks "how do I add a new provider" or "create a provider for X"
github-actions
by tddworksManage ClaudeBar's GitHub Actions CI/CD pipelines: build, test, and release workflows. Use this skill when: (1) Setting up secrets for CI/CD (certificate, API key, Sparkle key, Codecov) (2) Creating a new release — tag-based or manual workflow_dispatch (3) Triggering or explaining the build.yml, tests.yml, or release.yml workflows (4) Debugging release failures (signing, notarization, appcast) (5) Managing beta vs stable channels for Sparkle auto-updates (6) User says "release a new version", "push a tag", "set up CI secrets", "why did the release fail"
fix-bug
by tddworksGuide for fixing bugs in ClaudeBar following Chicago School TDD and rich domain design. Use this skill when: (1) User reports a bug or unexpected behavior (2) Fixing a defect in existing functionality (3) User asks "fix this bug" or "this doesn't work correctly" (4) Correcting behavior that violates the user's mental model
add-report
by tddworksGuide for adding new report cards to ClaudeBar that analyze local data sources and display metrics with comparison deltas. Use this skill when: (1) Adding a new report/analytics card (e.g., weekly summary, model breakdown, session stats) (2) Creating data analysis features that read local files and display aggregated metrics (3) Adding comparison cards that show "today vs previous" style deltas (4) Building any feature that follows the DailyUsage pattern (parse → aggregate → report → card)
improvement
by tddworksGuide for making improvements to existing ClaudeBar functionality using TDD. Use this skill when: (1) Enhancing existing features (not adding new ones) (2) Improving UX, performance, or code quality (3) User asks "improve X", "make Y better", or "enhance Z" (4) Small enhancements that don't require full architecture design For NEW features, use implement-feature skill instead.
implement-feature
by tddworksGuide for implementing features in ClaudeBar following architecture-first design, TDD, rich domain models, and Swift 6.2 patterns. Use this skill when: (1) Adding new functionality to the app (2) Creating domain models that follow user's mental model (3) Building SwiftUI views that consume domain models directly (4) User asks "how do I implement X" or "add feature Y" (5) Implementing any feature that spans Domain, Infrastructure, and App layers
implement-feature
by tddworksGuide for implementing features in asc-swift (App Store Connect CLI) following architecture-first design, TDD, rich domain models, and Swift 6.2 patterns. Use this skill when: (1) Adding new functionality to the CLI tool (2) Creating domain models that follow user's mental model (3) Building new CLI commands that consume domain repositories (4) User asks "how do I implement X" or "add feature Y" (5) Implementing any feature that spans Domain, Infrastructure, and ASCCommand layers
app-implement-feature
by tddworksGuide for implementing features following architecture-first design, TDD, rich domain models, and Swift 6.2 patterns. Use this skill when: (1) Adding new functionality to a Swift app (2) Creating domain models that follow user's mental model (3) Building SwiftUI views that consume domain models directly (4) User asks "how do I implement X" or "add feature Y" (5) Implementing any feature that spans Domain, Infrastructure, and App layers
improvement
by tddworksGuide for making improvements to existing asc-swift (App Store Connect CLI) functionality using TDD. Use this skill when: (1) Enhancing existing features (not adding new ones) (2) Improving output formatting, performance, or code quality (3) User asks "improve X", "make Y better", or "enhance Z" (4) Small enhancements that don't require full architecture design For NEW features, use implement-feature skill instead.
implement-feature
by tddworksGuide for implementing features following architecture-first design, TDD, rich domain models, and Swift 6.2 patterns. Use this skill when: (1) Adding new functionality to a Swift app (2) Creating domain models that follow user's mental model (3) Building SwiftUI views that consume domain models directly (4) User asks "how do I implement X" or "add feature Y" (5) Implementing any feature that spans Domain, Infrastructure, and App layers
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.