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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ltx-video
by broomvaSet up, configure, and run LTX-2/LTX-2.3 (Lightricks) for AI video and audio generation. Use when: (1) Installing LTX-2 locally or via ComfyUI, (2) Generating video from text, image, audio, or keyframes, (3) Running inference with the 22B dev/distilled models, (4) Fine-tuning with LoRA/IC-LoRA, (5) Configuring upscalers (spatial/temporal), (6) Optimizing VRAM usage (FP8, quantization, low-VRAM modes), (7) Building content-generation pipelines with LTX-2, (8) Writing prompts for video generation, (9) Integrating LTX-2 into ComfyUI workflows. Triggers on: ltx, ltx-video, ltx-2, lightricks video, text-to-video generation, video diffusion model.
livecoding
by broomvaAlgorave-grade livecoded music workflow — TidalCycles patterns (Haskell DSL driving SuperDirt over OSC) + Hydra-synth visuals (browser or VS Code Simple Browser via a local HTML page that loads hydra-synth from CDN). Wraps the boot ritual (SuperCollider → SuperDirt → Tidal → Hydra), a vibe-descriptor pattern generator (industrial, ambient, DnB, footwork, techno, algorave-glitch), and a reference tunnel-visuals page for the @lo.fi.sci.fi-projected aesthetic. Mac-first; ports to Linux with package-name swaps.
ltx-video
by broomvaSet up, configure, and run LTX-2/LTX-2.3 (Lightricks) for AI video and audio generation. Use when: (1) Installing LTX-2 locally or via ComfyUI, (2) Generating video from text, image, audio, or keyframes, (3) Running inference with the 22B dev/distilled models, (4) Fine-tuning with LoRA/IC-LoRA, (5) Configuring upscalers (spatial/temporal), (6) Optimizing VRAM usage (FP8, quantization, low-VRAM modes), (7) Building content-generation pipelines with LTX-2, (8) Writing prompts for video generation, (9) Integrating LTX-2 into ComfyUI workflows. Triggers on: ltx, ltx-video, ltx-2, lightricks video, text-to-video generation, video diffusion model.
revenuecast
by broomvarevenuecast — turn a real-world capability into a self-demonstrating, high-throughput generative-AI revenue engine (the "Kleos" method). It is "/skillify for revenue": skillify turns a workflow into a tested skill; revenuecast turns a capability into a revenue engine whose own output IS the advertisement. The bstack-native composition of the 2026 "show-then-sell-the-system" creator loop (realosias, aivideoskool, GenHQ): Brand-Lock -> Show -> Distribute -> Hook -> Sell -> Moat, where the showcased output's desirability + accessibility-via-AI creates demand for the method, and you monetize the reproducible system. Composes content-engine (the factory), content-creation, blog-post, seo-llmeo, arcan-glass, social-intelligence, strategy-skills, and symphony/arcan (autonomous runtime). Its deterministic core (scripts/revenuecast_check.py) gates an engine-instance manifest on the design canon — own-the-audience, a real moat (not leakable prompts), the compliance/survival pillar (FTC v. Air AI / EU AI Act Art.50 / NO
content-engine-cinema
by broomvaCinematic Generation Layer — the taste and technique library for premium AI content. Codifies start-frame doctrine, camera style vocabulary (Anderson/Fincher/Nolan/Villeneuve/Kubrick), node pipeline architecture, scene generation formulas, and 25+ design styles with exact prompts. Tier-1 generation via Higgsfield CLI (30+ models — Soul V2, Nano Banana 2, Veo 3.1, Kling 3.0, Seedance 2.0, Flux 2, Marketing Studio). Triggers on: 'generate content', 'cinematic', 'create scene', 'start frame', 'style library', 'generate campaign', 'AI filmmaking', 'higgsfield generate', 'soul cinema', 'marketing studio'.
bitnet
by broomvaMicrosoft BitNet — 1-bit LLM setup, inference, and benchmarking on CPU. Automates the full workflow: clone bitnet.cpp, create conda env, download GGUF models from HuggingFace, build optimized ternary kernels, and run inference. Supports official Microsoft models (2B) and community models (0.7B-10B). Use when: (1) setting up BitNet/bitnet.cpp for local CPU inference, (2) downloading and running 1-bit/ternary LLMs, (3) benchmarking BitNet vs full-precision models, (4) building edge/agentic inference pipelines without GPU, (5) converting HuggingFace models to GGUF for bitnet.cpp. Triggers on: 'bitnet', '1-bit llm', '1.58-bit', 'ternary model', 'ternary weights', 'edge inference', 'cpu inference', 'bitnet.cpp', 'bitlinear', 'no gpu inference'.
colab-remote
by broomvaOrchestrate Google Colab Pro/Pro+ GPU instances as remote training backends via SSH. Compounds /agent-browser (to launch Colab sessions and install colab-ssh) with SSH (to operate the runtime remotely). Use when: (1) launching a Colab notebook for GPU training, (2) running training jobs on Colab from the local terminal, (3) transferring datasets/checkpoints to/from Colab, (4) monitoring GPU utilization on a Colab instance, (5) integrating Colab GPU compute with /autoany EGRI optimization loops, (6) reconnecting to a Colab session after timeout. Triggers on: "colab", "colab-remote", "colab ssh", "colab training", "remote GPU", "colab pro", "train on colab", "google colab".
sdr-satellite
by broomvaSoftware-defined radio (SDR) and satellite reception toolkit — what to install, what you can hear from space, and how to compose the open-source stack (SatDump, SatNOGS, GNU Radio, rustradio, satkit). Covers hardware abstraction (SoapySDR, rtl-sdr-rs), DSP frameworks (GNU Radio, liquid-dsp, rustradio, radiorust), receiver UIs (SDR++, SDRangel, Gqrx, CubicSDR), satellite decoders (SatDump for NOAA APT, Meteor-M LRPT, GOES HRIT, Inmarsat), ground-station orchestration (SatNOGS network, sgoudelis/ground-station, Hamlib), orbit mechanics (SGP4 in Python + Rust, satkit astrodynamics, dSGP4 differentiable). Use when: (1) the user wants to receive satellite signals — weather imagery, ISS SSTV, telemetry, (2) building a ground station or scheduling observations on SatNOGS, (3) decoding TLEs and computing satellite passes, (4) composing SDR tooling into a Rust pipeline (e.g. for Life Agent OS, Opsis world-state events, or arcan/sensorium), (5) explaining what an RTL-SDR or HackRF actually does, (6) deciding between Sa
make-spec
by broomvaScaffold a substantive human-readable design doc (spec / plan / ADR / report / PR explainer) as native HTML using the workspace's canonical Broomva dark theme. Implements P18 (Format-Follows-Audience) for Category-C native artifacts — distinct from `bookkeeping render`, which projects Category-B markdown canonicals to HTML. Use when: (1) drafting a substantive design doc a human will actually read (>100 lines OR contains tables/diagrams/decision matrices), (2) writing an ADR (architectural decision record), (3) producing a plan a non-agent stakeholder will review, (4) writing a PR explainer for a substantive PR, (5) producing a report that synthesizes prior research. The skill provides theme.css + 5 templates (spec, plan, adr, report, pr-explainer) so the agent doesn't rebuild the 70-line :root + h1-h4 + table + callout boilerplate every time. Triggers on "spec", "plan", "ADR", "decision record", "design doc", "explainer", "report", "html spec", "html doc", "Broomva html", "dark theme spec".
claude-remote-sessions
by broomvaPer-channel remote sessions for Claude Code via Discord and Telegram — each channel, thread, or chat gets its own isolated Claude Code session via tmux, with per-channel access control, project-specific workdirs, and automatic CLAUDE.md chain loading. Includes session managers, watchdog daemons (auto-respawn, auto-discover, stale cleanup), thread context injection, session resume, workdir mapping, and launchd boot persistence. Use when: (1) setting up per-channel Discord/Telegram sessions for Claude Code, (2) managing multiple sessions across messaging channels, (3) auto-discovering new channels or threads, (4) spawning thread sessions with parent conversation context, (5) keeping remote agent sessions alive, (6) cleaning up stale sessions, (7) setting up Telegram per-chat sessions. Triggers on 'remote sessions', 'discord sessions', 'telegram sessions', 'per-channel discord', 'discord watchdog', 'telegram watchdog', 'spawn discord session', 'claude remote', 'channel session', 'thread session'.
content-creation
by broomvaFull-stack content creation pipeline: idea or reference to published blog post, audio narration, video, and social media distribution. Orchestrates research, reference extraction, storytelling, AI visual assets (Nano Banana, Veo 3.1), TTS audio (Voicebox, kokoro-tts, Edge TTS), Remotion video, and social copy into a complete content package. Use when: (1) creating a blog post, case study, or writing entry, (2) turning an idea or experience into structured narrative, (3) using a reference post/video (X, LinkedIn, YouTube) as inspiration, (4) generating visual content (AI images, clips, screenshots, GIFs), (5) generating audio narration for posts, (6) creating social media content (X threads, Instagram carousels) from long-form writing, (7) packaging and distributing content across platforms. Triggers on 'new post', 'blog post', 'case study', 'content creation', 'write about', 'content pipeline', 'social content from post', 'use this as reference', 'generate audio', 'tts', 'narration'.
procurer
by broomvaGrounded procurement research for any real-world need. Turn a problem ("reduce bedroom noise from the avenue", "add air conditioning", "hire an accountant", "buy a TV mount", "replace the kitchen sink") into 3–5 ranked alternatives spanning DIY-retail → mid-retail → specialty → contractor → consultant/turnkey, each with cited price bands (low / typical / high), confidence scores, locale-aware currency, and a final recommendation with budget. Output is decision-shaped (recommendation + budget envelope), not research-shaped (knowledge artifact).
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.