Docs Gen
Generate and maintain documentation
pm-prioritization
by majesticlabs-dev
Feature prioritization frameworks - RICE scoring, ICE scoring, and Opportunity Solution Trees.
optimize-agent-docs
by petekp
Build a retrieval-optimized knowledge layer over agent documentation in dotfiles (.claude, .codex, .cursor, .aider). Use when asked to "optimize docs", "improve agent knowledge", "make docs more efficient", or when documentation has accumulated and retrieval feels inefficient. Generates a manifest mapping task-contexts to knowledge chunks, optimizes information density, and creates compiled artifacts for efficient agent consumption.
code-comments
by petekp
Write clear, plain-spoken code comments and documentation that lives alongside the code. Use when writing or reviewing code that needs inline documentation—file headers, function docs, architectural decisions, or explanatory comments. Optimized for both human readers and AI coding assistants who benefit from co-located context.
document-conversion
by Harryoung
Convert DOC/DOCX/PDF/PPT/PPTX documents to Markdown format. Automatically detect PDF type (electronic/scanned), extract images to separate directory. Use this Skill when administrator onboards non-Markdown documents. Trigger condition: Onboard DOC/DOCX/PDF/PPT/PPTX format files.
large-file-toc
by Harryoung
Generate table of contents overview for large files. When onboarded Markdown file exceeds threshold (default 30KB), extract heading structure to create navigation file. Trigger condition: Markdown file size >= 30KB.
proposal-review
by petekp
Facilitate methodical review of proposals (technical designs, product specs, feature requests). Use when asked to "review this proposal", "give feedback on this doc", "help me review this RFC", or when presented with a document that needs structured feedback. Handles markdown files, GitHub gists/issues/PRs, and other text formats. Chunks proposals intelligently, predicts reviewer reactions, and produces feedback adapted to the proposal's format.
agent-fetch
by teng-lin
Fetch and extract full article content from URLs. Returns complete text with structure (headings, links, lists) instead of summaries. 7 extraction strategies, browser impersonation, 200-700ms.
best-minds
by Ceeon
模拟器思维：不问"你怎么看"，而是问"世界上谁最懂这个？TA 会怎么说？"。触发词：最强大脑、顶级专家、世界级、best minds、谁最懂这个
quicknode-skill
by quiknode-labs
Quicknode blockchain infrastructure including RPC endpoints (80+ chains), Streams (real-time data), Webhooks, IPFS storage, Marketplace Add-ons (Token API, NFT API, DeFi tools), Solana DAS API (Digital Asset Standard), Key-Value Store, gRPC streaming (Yellowstone for Solana, Hypercore for Hyperliquid), and x402 pay-per-request RPC. Use when setting up blockchain infrastructure, configuring real-time data pipelines, processing blockchain events, storing data on IPFS, using Quicknode-specific APIs, querying Solana NFTs/tokens/compressed assets, persisting state with Key-Value Store, or building low-latency gRPC streams. Triggers on mentions of Quicknode, Streams, qn_ methods, IPFS pinning, Quicknode add-ons, DAS API, Digital Asset Standard, compressed NFT, cNFT, getAssetsByOwner, searchAssets, Key-Value Store, KV store, qnLib, Yellowstone, gRPC, Geyser, Hypercore, Hyperliquid, HYPE, evm, rpc, ethereum, blockchain, solana, or x402.
agents-md-generator
by julianromli
Generate hierarchical AGENTS.md structures for codebases. Use when user asks to create AGENTS.md files, analyze codebase for AI agent documentation, set up AI-friendly project documentation, or generate context files for AI coding assistants. Triggers on "create AGENTS.md", "generate agents", "analyze codebase for AI", "AI documentation setup", "hierarchical agents".
agent-prompt
by treasure-data
Write effective system prompts for TD AI agents. Covers role definition, constraint specification, output formatting, tool usage instructions, and prompt structure patterns.
field-agent-deployment
by treasure-data
Best practices for developing, testing, and deploying production-ready Field Agents including R&D workflows, version control, testing strategies, and release management
rt-journey-create
by treasure-data
Create RT 2.0 Journeys (event-triggered) that activate when specific events occur, sending real-time data to external systems
Hedera Hackathon Submission Validator
by hedera-dev
This skill should be used when the user wants to validate, review, or score their Hedera hackathon submission before submitting. Triggered by phrases like "validate hackathon submission", "review my hackathon project", "hackathon scorecard", "check submission readiness", "score my hackathon", "submission review", "judge my project", "hackathon submission quality", or "improve hackathon score".
rt-triggers
by treasure-data
Create RT 2.0 Journeys (Real-Time Triggers) that activate in real-time when specific events occur, triggering activations to external systems via API
kaggle
by shepsci
"Unified Kaggle skill. Use when the user mentions kaggle, kaggle.com, Kaggle competitions, datasets, models, notebooks, GPUs, TPUs, badges, or anything Kaggle-related. Handles account setup, competition reports, dataset/model downloads, notebook execution, competition submissions, badge collection, and general Kaggle questions."
api-design
by rbarcante
Use this skill when designing or implementing REST APIs, endpoints, routes, or controllers.
tech-article-writer
by smallnest
技术干货文章创作专家,擅长用简练语言传递核心知识点,不遗漏关键信息
mcp-builder
by smallnest
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
slidev
by smallnest
Create and edit presentation slides using Slidev framework when user requests slides, presentations, or deck modifications
ship-learn-next
by michalparkola
Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.
yt-search-download
by joeseesun
YouTube 视频搜索、下载视频、下载字幕工具。结合 YouTube Data API v3 进行高级搜索，yt-dlp 下载视频/音频/字幕。 核心能力：全站关键词搜索、频道浏览、按时间/播放量/相关度排序、下载视频、提取音频（MP3）、下载字幕（中英文）、查看视频详情。 触发场景（任何涉及 YouTube 的操作都应使用此 skill）： - 搜索类："搜索YouTube"、"YouTube搜索"、"找YouTube视频"、"搜索[频道名]最新视频"、"查找[人名]的YouTube"、"[人名]最近更新的YouTube"、"YouTube上有什么关于XXX的" - 频道浏览类："浏览频道"、"看看[频道名]最新视频"、"[人名]最近发了什么视频"、"[人名]YouTube更新"、"查看[频道名]的视频列表" - 下载视频类："下载YouTube"、"下载这个视频"、"下载YouTube视频"、"把这个视频下载下来"、"保存视频" - 下载音频类："提取音频"、"下载音频"、"YouTube转MP3"、"只要音频" - 下载字幕类："下载字幕"、"提取字幕"、"获取字幕"、"下载YouTube字幕"、"中文字幕"、"英文字幕"、"提取文字"、"视频转文字"、"获取视频文本" - 视频信息类："视频详情"、"视频信息"、"这个视频多长" 关键词匹配：只要用户消息中出现 "YouTube"、"油管"、"YT"、"yt" 加上搜索/下载/字幕/频道/视频等动作词，就应触发此 skill。
framework-initiative
by rfxlamia
Framework for agents to understand implicit user intent, think before acting, and consider action impact. Uses STAR framework (Stop-Think-Analyze-Respond) to prevent literal execution that doesn't match context. Use when agent receives ambiguous requests, abstract commands like "fix", "improve", "delete all", or actions with potential wide-scope impact. Trigger on code modification requests without explicit scope constraints.
humanize
by rfxlamia
Transform AI-to-AI documentation into human-readable prose with personality. USE WHEN: Converting rigid AI-generated docs (checklists, templates, CAPS LOCK emphasis, perfect structures) into flowing, conversational documentation that feels like a colleague wrote it, not a robot. PRIMARY TRIGGERS: "humanize this doc" â Full transformation workflow "make this readable" â Quick readability pass "remove AI patterns" â Surgical pattern removal "add human touch" â Inject personality without losing precision INPUT: Markdown files with AI-agent patterns (checklist spam, rigid headers, template overload) OUTPUT: Flowing prose with burstiness, analogies, and occasional absurd touches CORE PRINCIPLE: Total war on AI rigidity. Destroy robotic tone while preserving technical accuracy.