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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dev-browser
by decebalBrowser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", or any browser interaction request.
feature-spec
by decebalFeature specification and planning guidelines for software engineers. This skill should be used when writing PRDs, defining requirements, managing scope, prioritizing features, or handling change requests. Triggers on tasks involving feature planning, specification writing, stakeholder alignment, or scope management.
ralph-tui-cn-beads
by decebalConvert PRDs to beads for ralph-tui execution using chronis (cn CLI). Creates an epic with child beads for each user story. Use when you have a PRD and want to use ralph-tui with chronis as the task source. Triggers on: create beads, convert prd to beads, beads for ralph, ralph beads, cn beads, cn create.
ralph-tui-cn-prd
by decebalGenerate a Product Requirements Document (PRD) for ralph-tui task orchestration using chronis (cn CLI). Creates PRDs with user stories that can be converted to beads for automated execution. Triggers on: create a prd, write prd for, plan this feature, requirements for, spec out, cn prd.
ralph-tui-create-beads-rust
by decebalConvert PRDs to beads for ralph-tui execution using beads-rust (cn CLI). Creates an epic with child beads for each user story. Use when you have a PRD and want to use ralph-tui with beads-rust as the task source. Triggers on: create beads, convert prd to beads, beads for ralph, ralph beads, br beads, cn beads.
ralph-tui-prd
by decebalGenerate a Product Requirements Document (PRD) for ralph-tui task orchestration. Creates PRDs with user stories that can be converted to beads for automated execution. Triggers on: create a prd, write prd for, plan this feature, requirements for, spec out.
create-plans
by decebalCreate hierarchical project plans optimized for solo agentic development. Use when planning projects, phases, or tasks that Claude will execute. Produces Claude-executable plans with verification criteria, not enterprise documentation. Handles briefs, roadmaps, phase plans, and context handoffs. NOTE — if the project already uses chronis (`.beads/` present) or another task tracker, do NOT run this as the top-level planner; chronis stays the queue and dependency tracker. Use this instead to author per-bead PHASE plans for in-bead execution detail (paste the PLAN.md into the bead description, or store it at .planning/phases/<bead-id>.md) — see CLAUDE.md "Planning skills" if the project has one.
xlsx-toolkit
by decebalRead, write, convert, diff, and analyze .xlsx (Excel) files with a single Rust binary built on umya-spreadsheet. Use when the user wants to inspect an unknown spreadsheet, extract sheet data to CSV/JSON, generate an xlsx report (with headers, formulas, freeze panes, autofilter, column widths), compare two workbooks, or measure column fill coverage (e.g. "what's actually populated in this spreadsheet?"). Triggers on: read xlsx, parse xlsx, convert xlsx to csv, generate xlsx, write excel file, diff two spreadsheets, coverage of a spreadsheet, what's in this .xlsx, summarize this excel file.
pr-review-coach
by decebalCompanion to the `pr-atom-reviewer` skill. Reads the review history stored in AllSource Prime (the `prime_*` MCP tools) and turns it into actionable improvements to how the reviewer works. Three jobs — (1) ingest post-merge outcomes for past reviews (was the split followed? did the PR cause a revert?), (2) cluster the repo's actual atom vocabulary so the calibration table reflects this team's work instead of generic examples, (3) surface contradictions across past reviews so the user can decide what to harden into the SKILL.md. Use this skill when the user asks "what have we learned from reviews", "audit my past reviews", "what should I tighten in the reviewer skill", "did my recommended splits get followed", "how is the reviewer doing", "weekly review retro", or runs a periodic skill-improvement pass. Trigger also on phrases like "PR retro", "review postmortem", "calibrate my reviewer", or when the user mentions wanting to update the calibration table or the SKILL.md for the reviewer.
verify-db-change
by decebalEnd-to-end verification of a database change (new tables, columns, indexes, writers/projectors) against real Postgres before review or merge. Replaces vibes-only "looks fine" sign-off with a structured proof block — schema applied per tenant, behavioural SQL exercised inside BEGIN…ROLLBACK on the live dev DB, app booted with the new wiring, and migration reversibility proved against a throwaway schema clone. Produces a PR-ready markdown excerpt with real captured output. Use this skill whenever the user asks to verify a migration, test a migration, prove a schema change works, verify a projector / writer / repository, "run any manual test needed to prove completion", or anything that means "show me the database actually works, not just the unit tests". Also use proactively when reviewing a PR that touches migrations or DB writers and the existing description has no real-Postgres proof. Designed for TypeORM + Nest + Postgres monorepos with multi-tenant DBs and Docker-Compose local infra, but the principles app
brainstorming
by decebalYou MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
pr-atom-reviewer
by decebalReview a local git branch as a pull request with a bias toward minimum disruption and minimum reviewer scope. Splits sprawling PRs into independently-mergeable atoms of work — each one a single end-to-end behaviour describable in 2-3 sentences, each shippable to the trunk on its own without any other atom in the plan being merged first — and demands a screenshot/video/recording proving the verifiable acceptance criterion. If the AllSource Prime MCP server is available (`prime_*` tools), the skill recalls prior reviews of the same repo to calibrate its judgement and records the current review for the next one. Use this skill whenever the user asks to review a PR, review a branch, prep a PR for review, check if a branch is ready to merge, or mentions "this PR is too big", "split this PR", "atomic commits", "scope creep in PR", "PR review checklist", or anything about getting a branch in shape before peers look at it. Trigger even if the user just says "look at my branch" in a code review context.
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.