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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h-diagnose
by m0n0x41dDiagnoses a failure with parallel rival-hypothesis testing — multiple read-only subagents test distinct explanations in parallel, then rank by evidence weight while keeping losing rivals visible so the root cause is found honestly, not just plausibly. Make sure to use this skill whenever the user reports something broken with an unclear cause — "tests fail", "test is failing", "X doesn't work", "Y crashes", "why is Z happening", "investigate this bug", "what's causing this", "the bug is unclear", "something's wrong with X", "X used to work and now doesn't", "this is flaky" — or any failure report where the next diagnostic step isn't already obvious to the user. NOT for feature requests (use h-frame). NOT for performance work with a known bottleneck (use h-frame). NOT for verifying a hypothesis already recorded in a DecisionRecord (use h-verify).
h-frame
by m0n0x41dFrames an engineering problem before any solution is explored — stabilizes the signal, names what is actually broken, declares acceptance criteria, and records a ProblemCard. Make sure to use this skill whenever the user proposes a refactor, rewrite, redesign, restructure, or rebuild without first naming the underlying problem or what acceptance looks like; whenever they say "let's rebuild X", "switch from Y to Z", "we should restructure", "I want to change A to B", "refactor this", "let's redo this" without stating success criteria; whenever a proposed solution arrives before the problem is defined; whenever scope is fuzzy and acceptance is unstated. Also catches explicit framing intent — "let me think about X", "before we solve this", "what's actually going on", "I want to understand X first". For broken tests or failing code with unclear cause prefer h-diagnose. For micro-decisions with rationale use h-note.
h-abduct
by m0n0x41dINTERNAL SUBROUTINE — used by h-diagnose for parallel rival-hypothesis generation. Manual invocation possible but the right user-facing entry point is almost always h-diagnose (which uses h-abduct internally with parallel testing). Generates ≥3 typed rival explanations for an observed signal per FPF B.5.2 abductive cycle. Do not auto-select this skill — when failure investigation is needed, select h-diagnose; when problem framing is needed, select h-frame.
h-commission
by m0n0x41dCreates a WorkCommission — bounded execution authority — from an active DecisionRecord. MANUAL ONLY: operator must explicitly type /h-commission. Never auto-invoked: commissions are execution-authority grants under Transformer Mandate. Runs freshness check, scope check, derives an ImplementationPlan, snapshots the autonomy envelope, then STOPS before execution unless explicit execute authority is granted. NOT for the decision itself (use /h-decide first). NOT for running tests or one-off tasks (the operator's coding agent handles those directly).
h-decide
by m0n0x41dRecords a binding DecisionRecord with full FPF DRR discipline — problem frame, decision/contract, rationale, consequences. MANUAL ONLY — the operator must explicitly type /h-decide. Never auto-invoked: per Transformer Mandate, the human principal records binding choices, not the agent. Use after framing, exploring, and comparing are done and a chosen variant is ready to commit. For tactical reversible changes (under 2-week blast radius) pass mode="tactical" with explicit _skips and _skip_reason. For irreversible / security / cross-team / public-API / data-migration changes pass mode="deep" — all DRR fields required, no skips accepted.
h-explore
by m0n0x41dGenerates 3–5 genuinely distinct candidate solution variants for a framed problem — each variant differs in KIND (not just degree), carries an explicit weakest-link so weak options surface before implementation, and optionally marks stepping-stones that open future search space. Make sure to use this skill whenever the user asks "what are our options", "how could we do X", "brainstorm approaches", "give me alternatives", "different ways to X", "what variants should we consider", "what else could we try", or whenever they are about to commit to one approach without having generated alternatives. Also use when a problem is framed but only one solution sits on the table. NOT for comparing existing options head-to-head (use h-compare). NOT for hypothesis testing on a failure (use h-diagnose).
h-note
by m0n0x41dRecords a micro-decision with rationale into the haft artifact graph — lighter than a full DecisionRecord but persisted so future sessions and conflict detection can surface it. Make sure to use this skill whenever the user says "remember that", "FYI for later", "note that we chose X", "side note", "let's record we ruled out Y", "remember we decided X", "for the record", "worth noting", "TIL", "important caveat", "save this thought" — or whenever a small choice with stated rationale belongs in project memory but does not justify the full DRR ceremony. The kernel rejects content-free notes — rationale is required. For binding choices use h-decide (manual-only). For framing problems use h-frame.
h-onboard
by m0n0x41dFirst-setup ceremony for a project that does not yet use haft — the agent reads the repository, drafts the minimum FPF carriers (target system, enabling system, term map) from observed code/docs, and presents them to the operator for review. The operator is NOT asked to author spec files from scratch — that defeats the value of having an AI agent. Make sure to use this skill whenever the repository has no `.haft/` directory yet, when the user says "set up haft here", "onboard this project", "initialize FPF", "first time using haft in this repo", "let's add haft to this project", "scaffold haft for this codebase" — or whenever they want to start recording decisions but the artifact graph is not scaffolded. NOT for ongoing work in a project that already has `.haft/` (use h-status). NOT for framing one specific problem (use h-frame).
h-reason
by m0n0x41dUmbrella for FPF-style structured reasoning in a haft project. Carries the full reasoning palette in one place: framing, exploration, comparison, verification, notes, plus slideument patterns (NQD, Goldilocks, BLP, Scaling-Law Lens). Make sure to use this skill whenever the operator wants structured thinking but the workflow isn't pre-named — phrases like "давай подумаем", "помоги разобраться", "let's think this through", "structured approach", "apply FPF here", "FPF reasoning", "haft this" — or whenever a request is ambiguous between framing, exploration, and comparison. Also the manual entry point: /h-reason or /h-fpf alias. For sharp signals dedicated skills still fire (h-frame, h-diagnose, h-explore, h-compare, h-verify, h-status, h-note, h-onboard, h-spec-cover); binding choices use manual /h-decide; commissioning uses manual /h-commission.
h-semio-review
by m0n0x41dINTERNAL SUBROUTINE — semiotic / fanout audit on a concept rename, deprecation sweep, or doc-vs-code consistency check. Walks all carriers (filenames, manifests, stale refs, review surfaces, dashboards, prompts) until fixed-point clean, because one-shot text replacement creates rework when carriers diverge. Manual invocation only. Do not auto-select for general work — for code review use Claude Code review; for FPF reasoning about decisions use h-frame / h-decide.
h-spec-cover
by m0n0x41dSurfaces uncovered files in modules that already have recorded decisions — highlights drift before it accumulates and suggests where new decisions are needed. Make sure to use this skill whenever the user asks "is X documented", "what's covered", "spec coverage", "drift detection", "what decisions apply here", "are we tracking this module", "what's undecided in X" — or whenever they are about to modify code in a module with existing DecisionRecords and should be reminded which decisions apply. Also use when /h-status flags large counts of undecided files in a module. NOT for looking up decisions affecting one specific file (use mcp__haft__haft_query action=related). NOT for verifying one decision's predictions (use h-verify).
h-status
by m0n0x41dProject state dashboard for haft — read-only consolidation of active problems, pending decisions, refresh-due artifacts, open work commissions, recent notes, and module coverage. Make sure to use this skill whenever the user asks "where are we", "what's pending", "what's stale", "project status", "what needs attention", "show me the state", "what's in flight", "what did we decide on X recently", "haft status" — or whenever a session resumes after a break and situational awareness is needed before deciding what to work on. Cheap, read-only, zero commitments. For verifying a single decision use h-verify. For managing commission lifecycle use h-commission.
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.