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Turn a structured LLM wiki into an interactive knowledge graph. It maps articles, topics, and sources, then adds entities and inferred relationships to help readers explore connections beyond the wiki’s explicit links.
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Relentlessly interview the user about THEIR plan or design — one question at a time — resolving each branch of a decision tree until shared understanding. Claude is the interviewer; the user is the interviewee. Triggers on "grill me", "stress-test this plan", "get grilled on my design", "interrogate my plan". Checkpoints every answer to docs/knowledge/<slug>.md so progress survives a context-window reset. Flags knowledge the user cannot answer as a gap to chase. Do NOT use to PROPOSE solutions or compare approaches (→ mk:brainstorming). Do NOT use to validate "is this worth building?" (→ mk:office-hours). Do NOT use when Claude should ANSWER questions about the repo (→ mk:ask-me). Do NOT use for a one-shot review of an existing plan (→ mk:plan-ceo-review). Do NOT use to apply named reasoning lenses to findings (→ mk:elicit).
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Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
中文简介正在准备中。当前显示原文说明。
Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
中文简介正在准备中。当前显示原文说明。
Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
中文简介正在准备中。当前显示原文说明。
Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
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Research topics across multiple sources, cross-check findings, and verify source URLs before producing a report with confidence labels. Choose from quick lookups to iterative investigations that preserve findings, with community sources prioritized for questions about public reactions.
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Deep-crawl any website from start URLs, return per-page LLM-ready text/markdown/HTML plus metadata (title, description, author, language, canonical URL, OG) and in-scope outbound links. Use when user mentions deep crawl website, recursive crawl, crawl a whole site, scrape entire website, scrape docs site, scrape documentation, scrape knowledge base, scrape blog, build RAG corpus, build vector database from website, knowledge base for chatbot, GPT knowledge files, llms.txt, sitemap crawl, BFS crawl, scrape with depth or page limit, include exclude URL globs, remove boilerplate, strip navigation header footer, website to markdown, website to text, multi-page extraction, bulk page scraping, clean markdown from URL, docs site to markdown corpus, site to clean corpus. Also applies to building RAG pipelines, indexing a customer site, syncing docs into a vector store, generating training corpora from any docs hub, or expanding a single start URL into a clean corpus of every reachable in-scope page.
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Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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Ingest public or authenticated knowledge bases and docs portals with Firecrawl browser. Use for JS-heavy docs, login-gated portals, paginated help centers, support knowledge bases, or structured JSON/markdown extraction from documentation sites.
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Ingest public or authenticated knowledge bases and docs portals with Firecrawl browser. Use for JS-heavy docs, login-gated portals, paginated help centers, support knowledge bases, or structured JSON/markdown extraction from documentation sites.
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Expert knowledge synthesizer specializing in extracting insights from multi-agent interactions, identifying patterns, and building collective intelligence. Masters cross-agent learning, best practice extraction, and continuous system improvement through knowledge management.
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Process large email, document, and transcript archives by routing items to AI models according to their importance. Rule-based privacy checks run before model calls, while extracted information becomes linked knowledge pages with progress checkpoints.
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Guides research into companies, markets, and technologies using public sources. Helps organize people and organizations, map their relationships, compare changes over time, assess source reliability, and structure reports with supporting links and sentiment trends.
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Turn papers, code, experiment logs, and notes into an ARA, a structured research package for AI agents. Organizes claims, evidence, verification plans, code stubs, and research decisions, then checks coverage and consistency across the generated files.
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Convert PDF, PowerPoint, Excel, and Word documents into structured Markdown for search and information extraction. Outputs include document metadata, a parsing confidence score, and the parser used, with fallback parsers available when earlier attempts fail.
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Extract text from local or downloaded PDFs using a sequence of tools. Try file reading, then pdftotext before a Python fallback, check readability, and document failures while clearly separating extracted content from knowledge-based additions.
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How to work in a Personal CRM project (the `entity-vault` starter pack, GBrain-compatible): a typed-entity vault of people, companies, meetings, and concepts, each a dossier with a rewritable summary plus an append-only timeline. Read when the project has these folders, OR when asked to capture notes about a person or company, log a meeting, prep for an upcoming meeting, or answer who someone is and what was last said. Carries the dossier convention and entity-extraction behaviors so that guidance does not live inside template bodies or folder descriptions. Complements the platform `open-knowledge` skill; does not replace it.
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Extract structured knowledge from source material. Comprehensive extraction is the default — every insight that serves the domain gets extracted. For domain-relevant sources, skip rate must be below 10%. Zero extraction from a domain-relevant source is a BUG. Triggers on "/reduce", "/reduce [file]", "extract insights", "mine this", "process this".
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Builds the gauntlet knowledge base from AST extraction and AI enrichment. Use when initializing or refreshing codebase knowledge for challenges.
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Route DeepKE knowledge extraction workflows across supervised extraction, data prep, triples, LLMs, and MCP tools.
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Operate the Agriculture_KnowledgeGraph agricultural Neo4j graph, Django demo, entity labeling, crawler, and relation-extraction workflows.
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Learn reusable 2D motion-generation knowledge from user-specified action resources with `/img2mo-learn <resource>`. Use when the user provides videos, extracted frame sequences, spritesheets, Spine assets, generated outputs, failed attempts, or reference motion folders and wants to summarize animation timing, pose beats, style traits, prompt patterns, extraction/cropping rules, or failure lessons into the project `img2mo-knowledge/` folder for later `/img2motion` generation.
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