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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parallel-planning
by malhashemiPlanning multiple workstreams? This skill identifies independent workstreams, spawns parallel planners, and analyzes dependencies between resulting plans. Perfect for large features spanning multiple components.
code-design
by malhashemiD-phase skill for the codesmith implementation workflow. Invokes render-design-doc-skeleton.py to overwrite the design.md placeholder with a tag-aware skeleton grounded in R-phase findings. Runs the always-interactive brain-surgery alignment conversation per Lock 6: agent presents each design section, human stress-tests via Question tool per substantive decision, iterate until aligned. Tracks the foundational-reversal strike counter per Lock 31. May identify multiple architecturally-distinct plans (NOT sub-tickets). Hands off to code-structure when D-phase is locked.
worktree-setup
by malhashemiCreate the codesmith implementation worktree and branch via worktrunk. Resolves the branch name from the parent ticket's primary tag (per tag-recipes/{tag}.md Branch prefix section) and the parallel-safety determination (single worktree for sequential plans; one worktree per plan for parallel-safe multi-plan). Invokes `wt switch -c <branch>`. Returns the worktree path and branch name to the caller.
pr-cycle
by malhashemiAutomated PR review cycle for codesmith when PR mode applies. Opens the PR, waits for Gemini Code Assist review via the `await_pr_review` tool, dispatches `codesmith-validator` to assess each unresolved thread against the codebase, implements approved fixes via `codesmith-worker`, responds to threads, requests re-review, and loops until Gemini approves or a context-handoff signal fires. Escalates to human for the final merge decision. NEVER auto-merges. Used at V-phase PR-review gate (Lock 11 gate 5) when PR mode applies; pairs with `local-merge` for the non-PR alternative.
code-verify
by malhashemiV-phase verification gate orchestrator per Lock 11. Invoked at four of the five V-gates: D-review (after D locks), P-review (after P locks), I-per-slice (after each slice's commit), final-integration (after all slices). The fifth gate, PR-review, lives inline in pr-cycle. For each gate: selects mandatory + conditional angles from verification-angle-table.md; dispatches `codesmith-validator` with per-gate prompt; receives JSON findings; renders into the bundle's verification.md via render-verification-report.py; handles binary verdict (PROCEED | BLOCKED) with severity-driven blocking; returns loop-back target to the caller when BLOCKED.
code-structure
by malhashemiS-phase skill for the codesmith implementation workflow. Produces the structural skeleton (the C-header-file analog) per Lock 7. For each plan identified in D-phase, generates the list of files to create/modify with purpose, type/interface/schema signatures, test file structure, and vertical-slice identification. Locks STRUCTURE not tactics — the actual TDD steps (test → impl → green → verify → commit) are P-phase's job. Spot-check user review; hands off to code-plan when structure is locked.
code-plan
by malhashemiP-phase skill for the codesmith implementation workflow. Converts the structural skeleton into per-slice tactical steps (TDD vertical: failing test → minimal impl → green → verify → commit) per Lock 8. Produces one plan doc per plan identified in D-phase. Runs the Parallel-Safety Evaluation per Lock 27 to determine whether multiple plans execute in parallel worktrees or serialize. Spot-check user review; no open questions in the final plan. Hands off to worktree-setup (or directly to code-implement if Work-Tree is bypassed).
code-orient
by malhashemiEntry-point skill for the codesmith implementation workflow. Detects dual mode (planning vs execution) from initial input shape — ticket reference → planning mode; bundle directory → execution mode. In planning mode: loads the `status: ready` ticket, scaffolds the implementation bundle via scaffold-implementation-bundle.py, transitions ticket status `ready → active`, and hands off to code-grill. In execution mode: loads the existing bundle (ticket + design + structure + plans + optional research), confirms preconditions, and hands off to code-implement. Detects PR mode vs local-only mode based on remote configuration and user confirmation.
code-implement
by malhashemiI-phase orchestration skill per Lock 10. Iterates over the locked plan's slices in dependency order. For each slice: invokes worktree-setup (first slice only or once per parallel plan); dispatches `codesmith-worker` subagent with slice scope (plan + slice boundary + verification commands + commit convention + worktree path); awaits PHASE_COMPLETE or NEEDS_DECOMPOSITION; on decomposition triggers mini-replan per Lock 19; on completion invokes `code-verify` for the I-per-slice gate. Owns the plan-as-living-doc's structural sections; worker owns the per-slice log subsections. Tracks decomposition strike count (abort after 3 consecutive on the same slice per Lock 17.2).
code-grill
by malhashemiQ↔R loop skill for the codesmith implementation workflow. Applies Kipling structure (What/How/Who/When/Why/Where) × grill-with-docs discipline × per-tag emphasis matrix to derive code-layer questions. Composes research dispatch instructions (in-memory, NOT a written artifact) per the 7 composable research patterns. Dispatches `researcher` across the hide-the-intent boundary — researcher receives questions + scope only, not the ticket. Synthesizes returns into the bundle's research file. Updates CONTEXT.md inline as terms surface. Scaffolds Decision Records when the three-condition gate fires. Loops until five readiness criteria are met (problem scoped, current state mapped, impact understood, edge cases identified, agent can articulate unprompted). Hands off to code-design when readiness is confirmed.
implementation-orchestration
by malhashemiThe Planner's guide to becoming an orchestrator! Manages the complete implementation lifecycle: worktrees, branches, spawning Implement agents, validation gates, and decomposition triggers. When ready to execute a plan, load this skill.
plan-review
by malhashemiCatch oversized phases BEFORE implementation begins! This skill reviews plans for sizing, dependencies, and completeness - flagging phases that need decomposition before a single line of code is written. Load this when validating plans pre-implementation.
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.