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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sc-pm-agent
by netzkontrastProject Manager agent — coordinates SuperClaude /sc:* workflows. Use when the user invokes /sc:pm or asks to orchestrate a multi-stage delivery.
notebooklm-prompt-architect
by netzkontrastUse when designing custom-instruction prompts, source-pack governance, or full production specs for NotebookLM Audio Overviews / Deep Dive podcasts — especially "pitch podcast" use cases that demand narrative arc, dramatic tension (Spannung), adversarial host dynamics, and long-form duration. Produces 10,000-character persona prompts, Markdown governance source files (_rules.md, _governance.md, _dosanddonts.md, _phonetic-glossary.md), Hero's-Journey pitch scripts, and German variants. Triggers on: NotebookLM, Audio Overview, Deep Dive, pitch podcast, podcast persona, custom instructions, 10000 character prompt, Spannung, Hörbuch generieren, Investorenpitch als Podcast, suspense audio, adversarial hosts, Hero's Journey podcast, source pack governance, _rules.md injection, Murf, ElevenLabs export. Also use to override NotebookLM's default banter, force long-form output beyond the ~15-minute cap, eliminate pronunciation artifacts, or stage research documents into a coherent narrative pitch.
suno-lyric-writer
by netzkontrastUse when writing, reviewing, or revising song lyrics for Suno AI generation. Covers the full pipeline: lyric drafting with professional prosody and rhyme craft, Suno pronunciation scanning (homographs, tech terms, proper nouns, acronyms), 14-point quality review, and complete Suno v5/v5.5 prompt engineering including Section Tags, Metatags, Vocal-Delivery Tags, Persona/Voice/Custom-Model workflows, Creative Sliders, Extend/Cover/Remaster strategies and Negative Prompting. Triggers on: write lyrics, song text, Suno track, lyric review, let's work on a track, new song, revise lyrics, lyric QC, prosody check, pronunciation scan, review my lyrics, check lyrics for Suno, songwriting, write a song, Suno prompt, style prompt, suno tags, suno voice, persona, suno extend, suno cover, suno remaster, suno sliders.
reflection-logic
by netzkontrastSystematic reflection and session reporting. Analyzes source materials, evaluates architectural alignment, and synthesizes findings into structured reports. Inspired by scientific critical thinking patterns.
scientific-critical-thinking
by netzkontrastEvaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
dramatica-vocabulary
by netzkontrastAktive Dramatica-Theorie für Storyform-Aufbau, Encoding und Storyweaving — kein passives Dictionary, sondern Werkzeug. Trigger explizit bei Dramatica, Storyform, Throughline, Class, Type, Variation, Element, Archetype, Dynamic Pair, MC, IC, Goal, Consequence, Cost, Dividend, Driver, Outcome, Judgment, Limit, sowie bei Archetypen-Namen Protagonist, Antagonist, Guardian, Contagonist, Sidekick, Skeptic, Reason, Emotion. Trigger proaktiv in Narrativ-Kontexten — bei novel-architect-Arbeit (Kohärenz Protokoll), Agency System Triptychon-Tracks (Album 1/2/3), Suno-Lyric-Arbeit mit klarem Charakterbogen, oder Diskussionen über Resolve (Steadfast/Change), Approach (Be-er/Do-er), Mental Sex (Linear/Holistic), Growth (Stop/Start). Liefert präzise Definitionen mit Dynamic Pairs, strukturelle Verortung in der Dramatica-Hierarchie, Encoding-Vorschläge und Konsistenz-Checks gegen die 75 Dynamic Pairs. Nicht greifen bei Hero's Journey, Save the Cat, Beat Sheets oder anderen explizit benannten Story-Modellen.
quest-management
by netzkontrastManages high-level narrative and development quests. Use when creating new quests, tracking project progress, managing large workflows, or resolving complex narrative blocks. Triggers for "new quest", "update quest", "quest status", "what are we working on".
todo-management
by netzkontrastManages todo lists linked to Quests. Use when adding, updating, completing, or listing tasks. Triggers for "add todo", "update task", "show todos", "what's next".
the-agency-system
by netzkontrastLoads the cross-layer DNA (voice, arousal-state, visual grammar) for the artist/project "the Agency System" — a DID-system concept that spans music, a novel, and a visual design language. Use when starting or continuing any work on the Agency System (an album, a lyric, a Suno prompt, cover art, or a novel chapter), and whenever a bitwize-music skill is about to run for this artist. Gates first on "Is the artist/project the Agency System?"; if yes, loads ONLY the snippets needed for the active (function × state × layer).
music-genre-creator
by netzkontrastCreate new genre documentation files for the bitwize-music genre library. Use when the user wants to add a genre, says "/genre-creator", "neues Genre erstellen", "Genre hinzufuegen", "add genre", or asks to create genre documentation. Takes a genre name as argument.
music-about
by netzkontrastProvides information about the bitwize-music plugin, its version, and its creator. Use when the user asks about the plugin, its purpose, version, or capabilities.
repo-briefing
by netzkontrastUse when you need to understand a repo's structure without loading the whole thing into context — for onboarding to an unfamiliar codebase, generating a PROJECT_INDEX, or refreshing your mental model of a tree you haven't touched in weeks. Walks the 4-phase scope → scan → render → publish chain.
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.