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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research-and-write
by iusztinpaulEnd-to-end workflow: research a topic and then write a LinkedIn post about it. Use this skill whenever the user wants the full pipeline — from a topic idea to a finished LinkedIn post. Triggers on: 'research and write a post about', 'create a LinkedIn post about [topic]', 'I want to post about', 'write about [topic] for LinkedIn', or any request that implies both researching a subject and producing a LinkedIn post from it. This is the go-to skill when the user gives you a topic and expects a finished post.
research
by iusztinpaulRun deep research on any topic using the Deep Research MCP server. Use this skill whenever the user wants to research a topic, gather information, find sources, or create a research document. Triggers on: 'research this', 'find out about', 'gather information on', 'I need to understand', 'deep dive into', or any request that involves investigating a topic.
write-post
by iusztinpaulGenerate a LinkedIn post using the LinkedIn Writer MCP server. Use this skill whenever the user wants to write a LinkedIn post, create social media content, draft a post from research, or generate a post with an image. Triggers on: 'write a post', 'create a LinkedIn post', 'draft a post about', 'turn this into a post', 'generate a post', or any request involving LinkedIn content creation. Also use when the user has a guideline.md and research.md ready.
implement-universal
by iusztinpaulHarness-agnostic version of `/implement`. Drives a single workshop ticket through the SWE→Tester loop in ONE conversation, with the role prompts bundled as `agents/software-engineer.md` and `agents/tester.md` instead of being launched as subagents. Resolves the ticket from `implement_yourself/tasks/`, creates an `implementing/from-scratch` branch (a fixed default — not derived from the ticket; subsequent tickets stack on top), adopts the software-engineer role to implement, then switches to the tester role to verify (logic tickets only). Loops on FAIL up to 3 times, moves the file to `tasks/done/`, then commits directly with `git commit -m`. Stops after one ticket. Trigger when the user types `/implement-universal`, asks to "implement task NNN without subagents", or is running in a harness (Cursor, Windsurf, plain Claude API client, etc.) that doesn't support Claude Code's `Task` tool.
implement
by iusztinpaulDrive a single workshop ticket through the inner SWE↔Tester loop. Resolves the ticket from `implement_yourself/tasks/`, creates an `implementing/from-scratch` branch (a fixed default — not derived from the ticket; subsequent tickets stack on top), launches the software-engineer agent to implement it, then routes by archetype — Tester runs on logic tickets, orchestrator spot-checks the SWE's AC walk on glue/bootstrap tickets, fast-path file existence check on docs tickets (Tester HARD-OFF). Loops on FAIL up to 3 times, moves the file to `tasks/done/`, then commits directly with `git commit -m`. Stops after one ticket — the human reviews the commit, talks through it, then re-invokes for the next ticket. Trigger when the user types `/implement`, asks to "implement task NNN", says "pick up the next ticket", or otherwise wants to ship one workshop ticket under supervision.
architecture-review
by iusztinpaulPeriodic architectural sweep of a codebase — reads existing ADRs to avoid re-litigating settled decisions, maps the current module/dependency graph and layering, surfaces 5–10 architectural smells with severity, and emits each finding as a refactor proposal that `/refactor` can consume directly. Trigger when the user says "/architecture-review", "audit the architecture", "what's wrong with this codebase", asks before a major version bump, or when long-horizon tech debt feels untracked.
create-pr
by iusztinpaulCreate or update a GitHub pull request for the current branch. Use this skill whenever the user says "create PR", "open PR", "update PR", "push and PR", "/create-pr", or when the PR workflow step is reached in the development workflow. Handles both first-time PR creation and updating existing PRs with new changes.
day
by iusztinpaulRun a single task through the inner SWE↔Tester loop with the human supervising in real time. SWE implements, Tester verifies (including the e2e adversarial pass), you review the diff and commit. No PM grooming, no PM acceptance, no PR Reviewer, no On-Call. Trigger when the user wants to ship one task under active supervision, or says "/day".
git-guardrails
by iusztinpaulInstall Claude Code PreToolUse hooks that block destructive git/shell commands before they execute — force-push to protected branches, `git reset --hard` of unstaged work, `git push --no-verify`, and `rm -rf` paths that escape the worktree. Smart-merges into the project's `.claude/settings.json` and writes one guardian script. Trigger when the user says "/git-guardrails", asks to harden Claude Code against destructive git commands, or is about to run `/night` unattended for the first time.
grill-me
by iusztinpaulAdversarially interview a feature spec before plan approval — surface every unresolved decision (auth model, error semantics, persistence, idempotency, observability, rollout, scope edges), draft codebase-grounded answers where possible, and ask the human only the questions that genuinely need their input. Output is a decision-resolved spec ready for PM grooming. Trigger when the user says "/grill-me", asks "is this spec ready", "poke holes in this", "interview me on this plan", or before invoking `/night` on a vague feature description.
night
by iusztinpaulRun the full agent-team pipeline end-to-end for a single feature — branch + worktree, PM grooms a Tasks Plan, human approves the plan, inner SWE/Tester loop per task, PM acceptance, push, parallel On-Call (CI) + PR Reviewer (diff), optional Self-Improve, hand the PR to the human to squash-merge. Trigger when the user wants to ship one whole feature unattended between two human gates, or says "/night".
refactor
by iusztinpaulPlan a refactor as an ordered, commit-grain Tasks Plan with structural acceptance criteria (test suite green at every step, no behaviour diff, named module/coupling invariants). Output is a feature-shaped plan that `/night` can execute end-to-end. Trigger when the user says "/refactor", asks "plan a refactor of X", "extract Y from Z", "split this module", "rename across the codebase", or describes a structural change with no user-visible feature behind it.
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.