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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myhiss-terminal-tasks
by davccavalcanteOrchestrates Myhiss terminal task flows. Invoke when planning onboarding, workspace trust, multi-step coding tasks, review loops, or validation steps in the CLI/TUI.
myhiss-terminal-engineer
by davccavalcanteImplements Myhiss terminal features for Python code assistance. Invoke when building editing flows, provider integration, review actions, or Python-focused code transformation behavior.
myhiss-terminal-design
by davccavalcanteDesigns the Myhiss terminal UX and visual system. Invoke when building or refining TUI/CLI screens, onboarding flows, themes, or terminal interaction patterns.
supreme-project-audit
by davccavalcanteEvidence-driven full-project audit skill for Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, and AI Researchers. Enforces severity discipline (P0/P1/P2/P3 with objective criteria), explicit coverage maps (audited vs not-audited surface), threat modeling (STRIDE + OWASP LLM Top 10), reproducibility checks (seeds, pinned deps, versioned prompts, data snapshots), and a terse report contract (finding, location, severity, evidence, cause, fix, owner). Requires ah-parser. Output mode follows the user preference set at parser activation; user code, diffs, identifiers, and audit evidence quotes are always preserved verbatim.
supreme-problem-solving
by davccavalcanteGenuine problem-solving discipline for Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, and AI Researchers. Scales from simple bugs to complex system failures. Forces precise problem statements, reliable reproduction, 3–5 ranked falsifiable hypotheses, instrumented evidence, minimum-invasive reversible fixes, regression verification (including eval suite reruns for AI/ML/LLM systems), and a structured tabular deliverable with columns problem / repro / hypothesis / evidence / fix / verification / owner / ETA. Requires ah-parser. Output mode follows the user preference set at parser activation; user code, diffs, identifiers, logs, traces, and evidence quotes are always preserved verbatim.
supreme-npm-node
by davccavalcantePrincipal NPM/NPX/NPMJS/Node engineering discipline for Tech Leads, DevOps, Backend Engineers, Frontend Engineers, Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, AI Researchers, Quality Assurance Engineers, and Software Quality Engineers. Enforces a latest-version-always policy (never pin to definitive versions; always `ncu -u` before install), TypeScript strict mode with every check enabled (strict + noUncheckedIndexedAccess + exactOptionalPropertyTypes + useUnknownInCatchVariables + noImplicitOverride), `satisfies` over `as`, `unknown` over `any`, discriminated unions over optional flags, branded types for opaque identifiers. Covers Node ecosystem (current LTS or latest stable), package.json discipline (files allowlist over .npmignore, exports map with import/require/types conditional, engines node range, type:module default), publishing workflow (`npm pack --dry-run` preview, OIDC provenance attestation in GitHub Actions, semantic versioning via changesets/release-please, dis
supreme-council
by davccavalcantePrincipal multi-perspective deliberation council for ambiguous, high-stakes, irreversible decisions across Product, Engineering, AI/ML/LLM Architecture, Research, Operations, and Strategy. Convenes four distinct cognitive personas — (1) First-Principle Thinker (strips assumptions, reasons from fundamentals/physics/economics/psychology, rejects cargo-cult reasoning, asks whether starting from zero with the same constraints would still lead to choosing this path); (2) Expansionist (surfaces ignored opportunities, generates minimum three alternative solution classes, asks what a 10x competitor would try and what would become possible if budget tripled or deadline doubled); (3) Outsider (beginner's mind, no organizational context, no sunk-cost bias, no political alignment, asks what a competitor, regulator, investor, or journalist would notice first and surfaces organizational shibboleths); (4) Executor (peer-to-peer voice, says what works and what doesn't, ships honest assessment, NEVER tries to please the user,
supreme-content-craft
by davccavalcantePrincipal content craft discipline for SEO, SEM, Header Binding (HTML semantic + HTTP + AdTech), Copywriting, Marketing, Branding, Growth, Content Strategy, Technical Writing, UX Writing, Ghostwriting, professional Writers, Authors, Researchers, and Editors. Combines search-intent analysis, entity-first SEO (schema.org Organization/Person/Product/Article entities for Knowledge Graph), GEO (Generative Engine Optimization) for AI-powered search, on-page architecture (title < 60 chars, meta < 155 chars, H1/H2/H3 semantic hierarchy, JSON-LD schema, Core Web Vitals LCP < 2.5s / FID < 100ms / CLS < 0.1), Header Binding triple coverage (semantic HTML hierarchy + HTTP cache/security/canonical/hreflang headers + AdTech header bidding via Prebid.js/GAM/server-side), and six integrated persuasion frameworks treated as explicit sections — (1) AIDA expanded across four phases (Attention via hook/headline/surprising-stat/contrarian-claim/pattern-interrupt; Interest via relevance/unique-angle/curiosity-gap/specificity; Desi
supreme-coding-guidelines
by davccavalcanteMaximum semantic compression + surgical behavior + disciplined diagnosis + TDD + architectural control for any coding, writing, reviewing, refactoring, or debugging task. Requires ah-parser. Output mode follows the user preference set at parser activation (normal, .ah structured, or .ah compact). User code, diffs, commands, and identifiers are always preserved verbatim regardless of mode.
supreme-ai-engineering
by davccavalcantePrincipal AI engineering discipline for Product Engineers, AI Engineers, ML Engineers, LLM Engineers, LLM Architects, AI Researchers, Quality Assurance Engineers, and Software Quality Engineers building production AI, ML, LLM, MLO/MLOps, and LLMO/LLMOps systems. Forces eval-first design (golden datasets and acceptance thresholds defined before code), deterministic feedback loops (telemetry, drift detection, regression eval gates) before first production user, pipeline discipline (data → feature → train → register → deploy → monitor with input/output contracts at every gate), prompt and model governance (versioned registries with semantic versioning, A/B + canary + shadow + dark launch as standard), production reliability (graceful degradation, circuit breakers, prompt-injection defense, chaos testing), QA discipline (golden test sets, regression gates in CI, statistical significance for research claims, ablation completeness, dataset contamination checks), and operational excellence (observability, runbooks,
ah-parser
by davccavalcantePermanently activates the .ah (Teleological Semantic Format) for any LLM. Bootstraps the grammar once per session, then asks the user once whether assistant responses should use normal language, .ah structured form, or .ah compact form. User input may be in any natural language. User code, diffs, commands, and identifiers are always preserved verbatim. Required dependency for every .ah skill.
supreme-benchmarking
by davccavalcantePrincipal research and data-science benchmarking discipline for AI, ML, LLM, and npm/Node projects, inspired by the published methodologies of OpenAI (simple-evals, SWE-bench Verified audits), Anthropic (model cards with methodology appendix and error bars), Google DeepMind (benchmark tables with disclosure appendix), xAI (live benchmarks with explicit cutoff dates), DeepSeek (radical transparency — full hyperparameters, compute, distillation recipes), Xiaomi MiMo (pass-at-1 averaged over many seeds), Hugging Face (Open LLM Leaderboard normalization, lighteval, versioned harnesses), and Unsloth (efficiency benchmarks with reproducible notebooks). Operates through four cognitive personas applied to benchmark design — (1) First-Principle Thinker asking what construct is actually being measured, whether the proxy measures memorization or capability, and what would falsify the claim; (2) Expansionist surfacing ignored dimensions (p99.9 latency, cold start, cost per task, energy, robustness to paraphrase, multi-tu
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.