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概要と使いどころ
Classify curriculum content into Know, Understand, and Do categories to align teaching and assessment approaches. Use when planning units, writing objectives, or selecting assessment methods.
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Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.
日本語の概要は準備中です。原文の説明を表示しています。
Documentation reconnaissance for takeover — find all docs, assess accuracy, freshness, coverage, and discoverability, and identify critical knowledge gaps. Use when asked "what docs exist", "documentation assessment", or "knowledge gaps".
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Build or audit knowledge base -- article structure, coverage gaps, deflection rate, and maintenance process. Use when asked to "build a knowledge base", "what docs are missing", "improve our self-serve rate", or "audit our help center".
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Expert guide for AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.
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Universal Digital Immortality Engine — Distill anyone or anything you care about into an interactive digital soul. 给灵魂开个 GitHub。 Trigger whenever user wants to: - Create, distill, or awaken a Relic, digital soul, persona, or clone from chat logs, memories, or voice ("create a relic", "digital twin of my grandma", "preserve memory of pet/team", "帮我唤醒/蒸馏/做个Relic", "保存/纪念XX的记忆", "Relicを作りたい/記憶を保存"). - Talk to, chat with, or remember a loved one, pet, mentor, or companion ("talk to grandma", "chat with my cat", "跟奶奶聊天", "召唤咪咪", "おばあちゃんと話したい"). - Preserve or distill workplace history, team culture, or expert knowledge ("archive team culture", "distill business expert", "飞书/Slack协作记忆蒸馏"). Supports: Humans, Pets, Relationships, Teams, Places, Moments, Public figures, Domain experts.
Checks whether each claim in a manuscript is supported by the source it cites, reading the project's per-source Markdown knowledge base. Flags contradicted, unsupported, overclaimed, out-of-scope, and misattributed claims with verbatim source quotes. Runs citation-check first, and stops at a pre-flight gate when the knowledge base is missing, incomplete, or holds unconverted PDFs. Use to verify that a draft's claims match the sources it cites.
日本語の概要は準備中です。原文の説明を表示しています。
Incremental LLM-friendly wiki generator for Obsidian note vaults. Use when: (1) Building wiki from notes, (2) Ingesting notes to wiki, (3) Obsidian LLM wiki, (4) Incremental knowledge base management. Triggers: 'build wiki from notes', 'ingest notes to wiki', 'Obsidian LLM wiki', 'incremental knowledge base'.
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Knowledge Extraction — captures and routes knowledge at work boundaries. Use when you discover a pattern, make a decision, find a distinction, or complete a sub-task.
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Build and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.
日本語の概要は準備中です。原文の説明を表示しています。
Knowledge systematization engine — analyze codebases, generate Personas, JTBD, Process Flows, technical docs, SOP user guides, API references. Output as Markdown or VitePress Premium. SEO-optimized, AI/LLM-readable. One scan = complete knowledge base.
日本語の概要は準備中です。原文の説明を表示しています。
Sync high-value dev knowledge (skills, decisions, lessons) into Google NotebookLM for cloud recall, podcasts, and flashcards. Pairs with Dockit/deep-search. Use for NotebookLM, nlm, knowledge base, skill sync, or cross-machine AI memory.
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AFFiNE is an open source workspace that combines docs, whiteboards, and lightweight databases in one product. This skill is useful when an agent needs to structure notes, maintain linked knowledge, or automate content workflows around AFFiNE workspaces and self-hosted deployments.
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Use when the user wants to build, refresh, or visualize the repo's knowledge graph — code structure plus the committed wiki/ — via graphify. Produces an interactive graph + a GRAPH_REPORT.md of God Nodes, surprising connections, and suggested questions. Trigger on "/graph", "build the knowledge graph", "graph the repo", "map the codebase", "refresh the graph", "visualize the second brain".
日本語の概要は準備中です。原文の説明を表示しています。
Parallel map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.
日本語の概要は準備中です。原文の説明を表示しています。
Integrate a new source into the project knowledge base. Wraps the agent-knowledge-updater agent. Source can be a local file path, a URL, or pasted content. The updater reads the source, classifies it, identifies which existing library files it touches, makes surgical updates or creates new files, rebuilds the shelf-index, and appends to log.md.
日本語の概要は準備中です。原文の説明を表示しています。
Build and query fully local RAG knowledge bases from documents (PDF, MD, code, etc.) using RLAMA and Ollama — no cloud, no data leaving the machine. Triggers on 'local knowledge base', 'search documents', 'document Q&A', 'RAG query', 'ingest files', 'semantic search'.
日本語の概要は準備中です。原文の説明を表示しています。
Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.
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デジタルプロダクト系の専門領域（UI/UX, マーケティング, コピーライティング, ASO, グロース等）の 暗黙知を抽出・言語化・構造化し、他のSkillで使えるエージェント定義と評価基準を生成するスキル。 素人では判断できない「良い/悪い」の基準を、AI が専門家レベルで調査・分析・言語化し、 再利用可能なドメインナレッジとして出力する。 Use when: 評価基準を作りたい、ドメイン知識を抽出したい、専門家の視点を言語化したい、 暗黙知を構造化したい、ベストプラクティスを整理したい。 Triggers: "ドメイン知識", "専門知識", "評価基準", "domain expertise", "expertise extract", "知識抽出", "暗黙知", "ベストプラクティス抽出", "評価基準を作りたい", "専門家の視点", "ドメインナレッジ", "domain knowledge", "expertise builder"
AI systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.
日本語の概要は準備中です。原文の説明を表示しています。
Reference Hildegard von Bingen's Physica natural history knowledge. Covers classification of plants, stones, animals, fish, birds, elements, and trees with their medicinal, symbolic, and practical properties. Enables cross- referencing between categories and application guidance. Use when exploring a specific plant, stone, or animal from Hildegard's perspective, researching medieval natural history and cosmology, cross-referencing properties across categories, or integrating Physica knowledge into health, spiritual, or creative practice.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user wants to create, maintain, query, or visualize a local Markdown knowledge base. ThinkWiki can initialize a wiki, import files or webpages, collect inbox items, answer from existing knowledge, save higher-value outputs, and generate HTML viewer and graph artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a knowledge artifact needs review before sharing or execution. Not for source or remote-system changes.
日本語の概要は準備中です。原文の説明を表示しています。
Use these skills when you need to discover and explore data assets in the Knowledge Catalog. It allows you to search for entries, lookup specific metadata, and explore aspect types to understand your data platform's assets.
日本語の概要は準備中です。原文の説明を表示しています。