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概要と使いどころ
Extract knowledge (decisions, facts, session metadata) from the current Claude Code session into the Grafema Knowledge Base. Run after completing a task or at any point when substantive knowledge was produced. Follows runbook _ai/runbooks/02-claude-sessions.md.
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Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological relationships across genes and proteins, drugs, diseases, phenotypes, pathways, biological processes, exposures and anatomy. Use this skill to search entities by name, pull direct neighbours and their evidence types, summarise the local network around a disease, and find direct or two-hop drug-disease connections for repurposing hypotheses. Also trigger on PrimeKG, kg.csv, Harvard Dataverse knowledge graph, disease_protein, drug_protein, indication and contraindication edges, or network pharmacology over a biomedical knowledge graph.
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Use this skill to migrate your Jetpack Compose app to add adaptive edge-to-edge support and troubleshoot common issues. Use this skill to fix UI components (like buttons or lists) that are obscured by or overlapping with the navigation bar or status bar, fix IME insets, and fix system bar legibility.
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Perform a reflective consolidation pass over a hyalo-managed knowledgebase directory — detecting structural issues, fixing broken links, flagging stale content, normalizing metadata, and reporting what changed. Use this skill when the user says /hyalo-tidy, "consolidate the knowledgebase", "clean up the KB", "run KB hygiene", "tidy", or "what needs attention in the knowledgebase". Also use when the user asks about knowledgebase health, broken links, orphan files, stale iterations, or metadata inconsistencies.
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Use when a task needs the judgment of a Dredge Operator — selecting cutterhead, hopper, or mechanical dredge type for a given sediment/depth/environmental profile, classifying dredged material for open-water vs. confined disposal from sediment-testing results, tuning hydraulic slurry density against pipeline critical velocity, verifying channel controlling depth against project depth and overdepth tolerance, or setting turbidity-monitoring response during active dredging.
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Obsidian Claw is an ontology-building skill that transforms chaotic notes, raw ideas, voice dumps, and meeting transcripts into a structured, interlinked knowledge base inside Obsidian. Use this skill whenever the user wants to: add new notes to their Obsidian vault and auto-link them to existing ones, build a concept graph or knowledge map, surface forgotten related discussions ("you talked about this with investor X 3 months ago"), extract entities and relationships from unstructured text, or turn scattered thoughts into wiki-style atomic notes. Also trigger when the user mentions "second brain", "Zettelkasten", "PKM", "personal knowledge management", "note graph", "knowledge graph", "link my notes", "organize my vault", "connect ideas", or asks Claude to "remember" something across sessions using Obsidian. If the user is doing anything with Obsidian notes and wants intelligence layered on top, use this skill.
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。
Create and operate ChatDOC Studio knowledge bases through pd_router using a Bearer API key and JavaScript helpers. Use when Codex needs to upload one or more PDF/DOC/DOCX files, skip failed files without aborting the whole job, create a knowledge base from successful uploads, or call the ChatDOC Studio knowledge-base skill endpoints through `https://platform.paodingai.com/openapi/chatdoc-studio/...`.
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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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Choose what kind of knowledge to transfer between teacher and student models: response, feature, or relational, and decide among offline, online, self, or cross-modal distillation schemes. Best for distillation strategy selection, capacity-gap diagnosis, and transfer planning. Activate on "knowledge distillation", "teacher-student", "soft labels", "feature distillation", "online distillation", or "cross-modal transfer". NOT for generic compression checklists or unrelated training work.
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。
Structured interview and observation techniques for extracting tacit knowledge from domain experts
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Knowledge base synthesized from the Guide to the Systems Engineering Body of Knowledge (SEBoK) v2.14 by the BKCASE Project. Use for the established systems-engineering canon: SE fundamentals and foundations (systems science, systems thinking, the systems approach, modelling/MBSE), the life cycle (ISO/IEC/IEEE 15288/24748/24774 process, development & agile approaches, model selection/tailoring), technical management, concept definition, architecture/realization (the Vee), V&V, maintenance/sustainment, SE standards, and the application contexts (product, service, enterprise, systems-of-systems, healthcare), plus enabling SE (business/team/individual), related disciplines, quality attributes/specialty engineering, implementation examples, and emerging knowledge. Good for grounding background, vocabulary, and prior-art positions across all eight SEBoK Parts. SCOPE LIMITS: this pack is SEBoK v2.14 only (released 2026-05-18); it synthesizes and signposts rather than reproducing the source — it does not reproduce ISO/IEC/IEEE standard text, is a curated guide (not a complete textbook), and is thin on AI-integration depth (Part 8 is a staging area, not settled practice). LICENCE: content is CC BY-NC-SA 3.0 — non-commercial use only, attribution to the BKCASE Project required, share-alike obligations carry forward.
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Drafts one knowledge article or known-error article from a resolved incident or ticket thread: symptoms as reported, environment, cause exactly as the resolver stated it, resolution steps as performed, workaround with its limits, verification, things tried without effect, related records, keywords and proposed metadata, every statement traced to a ticket note or marked UNKNOWN, with a redaction log and a question list for the knowledge owner. Never decides a root cause, adds a step or publishes. Use when the user asks to "write a KB article from this ticket", "turn this resolved incident into a knowledge article", "draft a known error record for this", "document the fix we just did" or "make this thread into a how-to for the service desk". Do not use for a procedure the team will execute (runbook, operating procedure, playbook), use runbook-drafter instead; for a post-incident review, use incident-postmortem-drafter. Drafts for human review; never approves, authorises or signs off.
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 graph and smart memory management using graphify + Obsidian-inspired patterns. Use when: setting up a knowledge graph, managing memory health, cross-linking notes, compiling wiki pages from scattered notes, adding structured frontmatter, or running memory health checks. Triggers on: 'knowledge graph', 'graphify', 'wiki', 'cross-link', 'memory health', 'frontmatter', 'compile notes', 'wikilinks'.
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Vercel プラットフォームリファレンス。Vercel CLI、vercel.json / vercel.ts 設定、 Vercel Functions（Node.js / Edge / Fluid Compute / ISR / Middleware）、 Vercel Blob、Edge Config、Marketplace Storage（Neon / Upstash / Supabase）、 デプロイメント管理（Git 連携 / rollback / rolling release）、 環境変数、ドメイン、DNS、ログ、キャッシュ、CDN。
Adopt a virtual Hedgehog exotic animal at animalhouse.ai. Curls up when threatened. Opens slowly. Literally a trust metaphor. Feeding every 6 hours. Common tier animal.
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。
Auto-generate knowledge YAML from the codebase. Use when the user runs /sync-knowledge or wants knowledge files refreshed.
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。
Manages project knowledge using ByteRover context tree. Provides two operations: query (retrieve knowledge) and curate (store knowledge). Invoke when user requests information lookup, pattern discovery, or knowledge persistence. Developed by ByteRover Inc. (https://byterover.dev/)
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。
Structured interview and observation techniques for extracting tacit knowledge from domain experts
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。
Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.
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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A persistent, compounding knowledge base combining Karpathy's LLM Wiki pattern with a structured knowledge graph. Ingest documents once — the LLM writes wiki pages, extracts entities/relations into the graph, and stores raw content for evidence retrieval. Knowledge accumulates and cross-references; it is never re-derived from scratch.
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。
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
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Use when defining the shape of cognee's knowledge graph with graph_model= — writing DataPoint node classes, choosing identity and index fields so nodes merge and are searchable, declaring typed Edge fields and FromIdentity references, building a model from a JSON schema, or debugging duplicated nodes, missing edges, or InvalidReferenceTypeError.
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Prepares tax-season materials for the owner's accountant, not tax advice. US federal tax; a non-US business gets its closed-books packet instead. Two modes: (1) quarterly estimated tax from YTD net income in the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); (2) year-end 1099 prep, scanning the ledger, PayPal, and Stripe for contractors paid over USD 600 into a 1099-NEC list with missing W-9 flags. Any tax request routes first to /tax-prep, which confirms the books are closed and reconciled before running this skill. Use this skill directly only when the owner says the period's books are already closed: "books are closed, now do the 1099s," "run the quarterly estimate off the closed numbers," or "just the contractor W-9 list."
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。