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Found 1,051 Skills
DPoP-signed (RFC 9449) authenticated calls to Alien-aware services. Discover any Alien-aware service's manifest at /.well-known/alien-agent-id.json, render its operations as actionable markdown, emit DPoP headers for one request, or one-shot a signed HTTP call with the agent's identity attached. Use when the user gives you a URL on an Alien-aware service (alien-api.com, alien.org, agent-sso.*), asks to call an Alien-aware endpoint, asks what an Alien-aware service can do, or mentions DPoP, agent-bound access tokens, or `cnf.jkt`.
Loads documents fully into the main agent's context so the agent can answer questions, summarize, or work with that content in subsequent turns. Use whenever the user wants to ingest, read, study, review, absorb, or pull in documents — especially when they say things like "load these docs", "read all of these", "ingest this folder", "pull in these PDFs", "load all docs in X", or paste a list of file paths/URLs and ask you to read them. Handles local files (text, code, markdown, PDFs, notebooks, images), entire folders (recursively), and remote URLs. The skill is single-turn — once the agent reports "DONE", it deactivates until the user invokes it again.
Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Two responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, and (2) run ruff against any Python files Claude has just generated or edited. Stops at "the touched files pass `ruff check`." TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run on the touched files; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and the project has no `ruff.toml` at its root yet — drop the bundled template; (3) the user asks about lint, format, docstring style, or reaches for `black` / `isort` / `flake8` / `pydocstyle` (redirect to ruff — the stack's canonical linter, owned by `data-science-python-stack` Tier 1). SKIP when: the project is non-Python; the only edits in this turn are to Markdown / TOML / JSON / YAML; the file lives in a third-party vendored directory the user doesn't own. HOW TO USE: run ruff manually on the files you just touched — do not configure a PostToolUse hook for this. **Read the "Stop conditions" block and emit the Pre-flight checklist as visible text in your response — both are mandatory before running ruff.**
An official AI mind map generator developed by ProcessOn, focusing on converting content such as natural language, Markdown, long text, documents, web pages, and image text into professional, clear-structured, well-layered, and editable mind maps with one click. Whether it's article summarization, data organization, document decomposition, knowledge point induction, learning path sorting, or reading notes, paper literature sorting, meeting minutes extraction, work report summary, outline generation, project task decomposition, brainstorming and idea generation, this skill can quickly generate professional mind maps to help users transform scattered content into clear structured knowledge. This skill supports 7 professional graphic layouts including mind maps, logic diagrams, organizational charts, fishbone diagrams, timelines, tree diagrams, and table diagrams, and is deeply integrated with the ProcessOn online collaboration platform. The generated mind maps can be edited online, collaboratively modified, and efficiently reused, suitable for scenarios such as office work, study review, scientific research reading, knowledge management, and scheme planning. Note: This skill is mainly used to generate mind maps and knowledge structure brain maps, and is not applicable to the generation of process or technical charts such as flowcharts, swimlane diagrams, sequence diagrams, system architecture diagrams, ER diagrams, and Mermaid diagrams.
Spawning Plan. Use when user wants to spawn agents, create a team, or coordinate multiple agents. Automatically gathers context, asks team topology questions, outputs clean TEAM PLAN markdown, and gets user approval. 3 steps: context gathering → questions → present plan. **CRITICAL**: MUST NOT SPAWN AGENTS SKIPPING THIS SKILL, USE ALWAYS.
Explain how CE.SDK Web features work — concepts, architecture, and workflows. Covers React, Vue.js, Svelte, Angular, Electron, Vanilla JavaScript, Node.js, Nuxt.js, Next.js, SvelteKit. Use when the user says "explain", "how does X work", "walk me through", "what is", "describe", or wants to understand a CE.SDK concept at a conceptual level for Web development. Generates custom markdown explanations with diagrams and code examples. Not for looking up existing docs (use docs-{framework}), not for writing implementation code (use build). <example> Context: User wants to understand how text layers work user: "Explain how text layers work in CE.SDK" assistant: "I'll use /cesdk:explain to generate a detailed explanation." </example> <example> Context: User needs a concept explained in their context user: "How does the block hierarchy work for video editing?" assistant: "Let me use /cesdk:explain to create a custom explanation for video block hierarchy." </example> <example> Context: User needs to understand a workflow user: "Walk me through the asset loading pipeline" assistant: "I'll use /cesdk:explain to explain the asset pipeline." </example>
Web scraping and search CLI returning clean Markdown from any URL (handles JS-rendered pages, SPAs). Use when user requests: (1) "search the web for X", (2) "scrape/fetch URL content", (3) "get content from website", (4) "find recent articles about X", (5) research tasks needing current web data, (6) extract structured data from pages. Outputs LLM-friendly Markdown, handles authentication via firecrawl login, supports parallel scraping for bulk operations. Automatically writes to .firecrawl/ directory. Triggers: web scraping, search web, fetch URL, extract content, Firecrawl, scrape website, get page content, web research, site map, crawl site.
한글(HWP/HWPX) 문서를 다양한 포맷(Text, HTML, ODT, PDF)으로 변환하고, Markdown/HTML을 HWPX로 생성하는 작업을 도와줍니다. LLM/RAG 파이프라인을 위한 문서 처리, 청킹, LangChain 연동을 지원합니다.
Comprehensive automation for Letterly transcriptions. This skill exports the latest CSV from Letterly, processes "magic" notes into Obsidian markdown with custom metadata, semantically links them using a vector database, and moves them to the final Transcriptions directory. Use when the user asks to "process new letterly transcriptions", "sync letterly", or "import magic notes from letterly".
Web content extraction via Jina AI Reader API. Three modes: read (URL to markdown), search (web search + full content), ground (fact-checking). Extracts clean content without exposing server IP.
Comprehensive tool for interacting with rednote (xiaohongshu,小红书) platform. This skill enables users to search for posts by keyword, extract content from specific notes in markdown format, and perform interaction actions like liking, commenting, collecting, following, and publishing. Use this when users need to engage with content from xiaohongshu.com.
Comprehensive Obsidian vault management. USE WHEN obsidian, vault, note, daily note, PARA, inbox, knowledge capture, dataview, DQL, search vault, .base, bases, wikilink, frontmatter, second brain, markdown syntax, obsidian.nvim, OR obsidian API. Python-powered tools for search, creation, and vault health.