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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broll-cleanup
by jperrelloDeferred end-of-run B-roll cache eviction. For ONLY the B-roll source video_ids this run ingested (from each broll_plan.json's ingested_video_ids), runs `mcptube remove <id>` AND deletes the local work/<id>/broll/*.mp4 cache. Never touches the podcast source, unrelated library videos, or broll_plan.json (placement metadata persists for editors). Runs once at the very end of a whole pipeline run.
fill-vertical
by jperrelloReframe a horizontal (16:9) clip to 9:16 (1080x1920) by punching in to FILL the frame — never letterbox, no blur bars. Detects scene cuts and computes ONE static crop box per shot. Clusters face signatures across all shots to find the dominant speaker (storyteller); on multi-face shots it picks the speaker by lip-activity biased toward that identity; on no-face shots it crops toward the OpenCV-saliency centroid. A non-speaking reaction/listener shot is framed LOOSER so the short never dwells hero-framed on the wrong person. Faces are framed ~45% of frame height with the eyeline on the upper third, capped at ~2x upscale. Replaces fit-vertical in the canonical chain.
verify-bookends
by jperrelloPost-edit verification of a short's opening and closing 1.5s. Claude sees a 3-frame strip from each end plus the trimmed transcript text around each end and returns {action:keep}, {action:trim,t0,t1} (inward-only), or {action:drop,reason}. Checks three things — (1) cleanliness (partial words, breath cutoffs, off-shot frames); (2) opening-hook strength (snap t0 inward to a stronger hook line if the first 3s is pure setup); (3) payoff landing (snap t1 to ~80ms past the payoff word when the tail trails into filler). Runs after tighten-pace, before fit-vertical. Inward-only — bookend-trim's outward pass already had its chance. Drops the span only on cleanliness failures requiring >2s of trim — never on hook-weakness alone.
structured-arguments
by jperrelloUse this skill when working with labeled thesis arguments — creating, referencing, or weaving definitions (D), views (V), arguments (A), alternative views (AV), counter-arguments (CA), rebuttals, or barriers (B) into the thesis. Trigger when the user mentions argument IDs, cross-references, or wants to build/extend the argument scaffold.
loudnorm
by jperrelloNormalize audio loudness to broadcast levels using ffmpeg's two-pass loudnorm filter. Measures the input, then applies normalization with the measured parameters. Use as a final-stage audio leveling step.
qc-clip
by jperrelloSanity-check a rendered short with ffprobe. Verifies duration is in range and file size is non-trivial. Pass/fail boolean plus a short reason. Use as a final gate before saving a render.
sfx-beats
by jperrelloMix synthesized riser/hit/stinger SFX into a short at detected tension peaks. Riser ends at the first pivot word ('but'/'therefore'/'so'/etc.) inside the middle 60% of the clip, a low impact hit lands at the loudest RMS-per-second peak after it, a soft outro stinger sits in the last 0.4s. All SFX synthesized with stdlib `wave` — no external assets.
transcribe
by jperrelloTranscribe a video/audio file to a JSON transcript with word-level timestamps using local whisper.cpp + a GGML model. Use when you have a media file and need text + word timing for downstream subtitle burning or segment ranking.
zoom-punch
by jperrelloQuick punch-in zooms at a clip's loudest words — the standard retention-edit emphasis beat. Deterministic, no Claude: per-second RMS peaks snapped to word starts pick 1-4 punch moments (4s+ apart, clear of the title card and the tail); each gets a ~0.6s 10% zoom pulse (fast attack, hold, smooth release) cropped toward the upper third so the eyeline holds. Runs on the 1080x1920 vertical clip BEFORE broll-composite and burn-subtitles so cutaways and captions never warp.
like-subscribe-overlay
by jperrelloOverlay the branded like/subscribe CTA animation — channel gem avatar + @C0BALT_CUT handle + subscribe/like/bell click choreography — for ~4 seconds WITHIN THE FIRST THIRD of a finished short (start clamped to end by the 1/3 mark, floored after the ~2.5s title card; early placement so viewers who bail never miss it). The animation source is cta.html (HTML+CSS+JS driving the pill pop-in, cursor moves, click punches, bell ring, thumb fill); build-cta.sh renders it frame-by-frame via headless Chromium (Playwright, omitBackground) and ffmpeg-encodes to assets/cta.mov (ProRes 4444 with alpha).
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.