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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apply-dependency-prs
by fjrevoredoUse this skill whenever the user asks you to apply dependency update PRs from GitHub — Dependabot bumps, version updates in package.json, or merging dependency PRs. Use this even if the user just says "apply this PR" or "merge these PRs" and the PRs change package.json. This skill handles the dual-lockfile setup (bun.lock + package-lock.json), detects partially-applied PRs, catches stale lockfiles, resolves overlapping version bumps across multiple PRs, and runs the full validation suite after applying. Do not apply Rust/Cargo dependency PRs with this skill.
diagram-maintainer
by fjrevoredoMaintain, update, regenerate, and review Mermaid and D2 documentation diagrams for this repo. Use when working in docs/diagrams/, editing .mmd or .d2 sources, syncing diagrams to current code, regenerating SVGs, checking light/dark variants, or reviewing diagram readability and semantic drift. Triggers: mermaid, d2, docs/diagrams, mmd, svg diagram, architecture diagram, flow diagram, unlock flow, save-entry flow.
flathub-maintenance
by fjrevoredoDiagnose, fix, and manage Mini Diarium's Flatpak/Flathub packaging across the multi-repo ecosystem. Use when Flathub builds fail, the manifest or vendored sources (cargo-sources.json, node-sources.json) need regeneration, the runtime or permissions need bumping, AppStream metadata needs updating, or a Flathub PR needs manual intervention. Also use for initial Flathub submission changes, store listing updates, and any task touching flatpak/, .github/workflows/flathub-publish.yml, or the Flathub repo. Triggers: flathub, flatpak, Flatpak, Flathub, flatpak build, flathub build, vorarbeiter, flatpak-builder, flatpak manifest, flatpak publish, cargo-sources, node-sources, runtime bump, GNOME runtime, SDK extension, AppStream, metainfo, flathub PR, flathub publish, flathub token, flatpak permission, finish-args, flathub submission.
implementation-review
by fjrevoredoReview a completed implementation against its plan, decision log, code changes, tests, docs, and project best practices. Use when asked to assess whether an implemented TODO, roadmap item, PR, feature, refactor, or agent output is up to spec, maintainable, simple, high quality, and ready to merge. Produces a markdown report with assessment and actionable fixes.
manual-planning
by fjrevoredoCreate, update, review, and execute manual Markdown implementation plans when harness planning mode is not being used. Use when the user asks for a plan file, manual plan, implementation plan, execution plan, roadmap, task checklist, planning document, or agent-maintained plan with statuses, validations, milestones, approval gates, and cleanup steps.
pre-release
by fjrevoredoPre-release checklist for Mini Diarium. Run before tagging a release: verifies version consistency, archives completed TODOs, generates latest-changelog.md, optionally adds a release notification, and stamps the CHANGELOG date. Use when preparing a feature branch for merge and tagging.
review-external-pr
by fjrevoredoReview an incoming pull request from an external or first-time contributor to Mini Diarium. Covers correctness, PHILOSOPHY.md alignment (all 6 principles), project best-practices compliance, and technical quality. Produces two artifacts: an internal analysis report and a ready-to-post draft response, both written in plain human prose. Use this skill whenever someone asks you to review, assess, evaluate, or respond to a GitHub pull request — especially from external contributors. Triggers on phrases like "review this PR", "what do you think of this PR", "should we merge this", "respond to this contributor", "analyse this pull request", or when a GitHub PR URL or number is mentioned alongside a review intent.
skill-improver
by fjrevoredoCaptures lessons from completed tasks and turns them into concrete improvements. Triggers proactively after high-ceremony tasks (build failures, multi-hour sessions, production incidents) and on-demand when the user asks for "post-mortem", "reflect", "improve the skill", "lessons learned", "what went wrong". Covers skills, repo artifacts (scripts, CI, configs), and process changes. For small/obvious fixes, edits directly. For structural changes, produces a plan using the manual-planning format. Use this whenever a task surfaced gaps in tooling, documentation, or automation.
solidjs
by fjrevoredoSolidJS framework development skill for building reactive web applications with fine-grained reactivity. Use when working with SolidJS projects including: (1) Creating components with signals, stores, and effects, (2) Implementing reactive state management, (3) Using control flow components (Show, For, Switch/Match), (4) Setting up routing with Solid Router, (5) Building full-stack apps with SolidStart, (6) Data fetching with createResource, (7) Context API for shared state, (8) SSR/SSG configuration. Triggers: solid, solidjs, solid-js, solid start, solidstart, createSignal, createStore, createEffect.
sync-lockfiles
by fjrevoredoRegenerate both npm lockfiles after any manual change to package.json. Use this skill when the user has added, removed, or bumped a dependency in package.json and needs bun.lock and package-lock.json kept in sync. Triggers: "sync lockfiles", "update lockfiles", "I edited package.json", "regenerate lockfiles". Distinct from apply-dependency-prs, which handles the PR-discovery workflow — this skill handles lockfile sync only.
tauri-agent-dev
by fjrevoredoSpawn, probe, and stop Mini Diarium's live Windows Tauri dev app with WebView2 CDP enabled, then hand control to agent-browser for real UI inspection. Use this whenever the user wants to manually test the real desktop UI, drive the dev app, verify a bug or preference in the actual window, inspect localStorage, take a real screenshot, or "actually try it in the app" instead of relying only on unit tests or WDIO. Triggers: manually test the UI, drive the dev app, verify in the real UI, agent dev mode, spawn the dev app, open the running app and check, inspect the live Tauri window.
todo-manager
by fjrevoredoEnd-to-end management of TODO items in `docs/todo/TODO.md`. Covers creation with auto-assigned IDs, status tracking, archival of completed items, and format validation. Use when the user asks to add a new TODO, check TODO status, archive completed items, or validate the TODO system. Triggers: TODO, todo item, add todo, create todo, todo status, archive todo, validate TODO, TODO ID, TODO-0001, check TODO, todos, list todos, todo list.
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.