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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agr-cli
by computerlovetechInstall, share, sync, and create AI agent skills across coding tools (Claude Code, Cursor, Codex, OpenCode, Copilot, Pi) using the agr CLI. Use whenever the user mentions agr, agr.toml, agr.lock, agrx, or asks to: add a skill ("install the pdf skill", "agr add ..."), sync agent resources across tools, share skills with their team, scaffold a new SKILL.md, run a skill ephemerally (agrx), set up a repo to manage AI agent dependencies, or configure tools/sources/instruction-syncing. Also use whenever an agr.toml or agr.lock is present in the project and the user is doing resource-management work.
agr-release
by computerlovetechRelease process for the agr package. Handles version bumping (major/minor/patch/beta), changelog updates, pre-release quality checks, git tagging, and monitoring the GitHub Actions publish pipeline. Use this skill whenever the user wants to cut a release, bump the version, publish to PyPI, or asks about the release process — even if they just say "let's ship it" or "time for a new version".
skill-debrief
by computerlovetechDebrief an AI agent skill (SKILL.md) after using it — capture session feedback or a retrospective and fold it back into the skill. Use whenever the user says: "debrief the X skill", "let's debrief X", "retrospective on X", "feedback on X skill", "improve the X skill", "update the X skill", "let's revise X based on what we just did", "the X skill should also handle Y", "X didn't trigger when it should have", or otherwise wants to capture lessons from a session back into the skill that drove it. Handles in-repo skills (under skills/) by editing the source, committing, and re-installing via `agr upgrade`. Handles remote/upstream skills by offering to fork them in-repo or to file a GitHub issue via `gh`. Do NOT use for greenfield skill authoring (a separate concern — see `anthropics/skills/skill-creator`) or for installing / syncing / removing skills (use the `agr` CLI directly).
brand-guidelines
by computerlovetechApplies Ralphify's official brand colors, typography, and visual style to any artifact — landing pages, slides, diagrams, social graphics. Use when Ralphify's look-and-feel should be applied.
ralph-cli
by computerlovetechBuild, run, and debug autonomous AI coding loops with the ralph CLI from ralphify (https://ralphify.co). Use whenever the user mentions ralphify, ralph, RALPH.md, "ralph loop", "loop engineering", `ralph run`, `ralph scaffold`, or asks to: set up an autonomous agent loop, run a coding agent (Claude Code, Codex, …) repeatedly with fresh context, write or fix a RALPH.md frontmatter/prompt, wire commands and {{ placeholders }} into a loop, choose or configure an agent for a loop, run loops concurrently.
ralphify-cowork
by computerlovetechSet up and run autonomous AI coding loops with ralphify — no coding knowledge needed. Handles installation, creation, running, and tweaking.
release
by computerlovetechRun the full ralphify release process — analyze changes, update docs, bump version, commit, push, create GitHub release, verify CI, and generate social content. Use when cutting a new release. Optional arg "patch", "minor", or "major" to skip the version bump prompt.
brand-guidelines-computerlove
by computerlovetechApplies Computer Love's official brand colors and typography to any sort of artifact that may benefit from having Computer Love's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
markdown2pdf
by computerlovetechConvert a Markdown file to a styled PDF and iterate on the design visually. Use when the user asks to render markdown as a PDF, style a PDF, produce a printable handout, or tune the look of a generated PDF. Ships a neutral default theme; pairs with any styling skill that provides a CSS file.
smoke-and-mirrors-ui-prototyping
by computerlovetechBuild a high-fidelity prototypes with everything faked.
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.