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Tencent IMA OpenAPI skill for notes and knowledge base management. Use when user mentions knowledge base, notes, memos, file uploads, web page collection, or knowledge search. Supports notes CRUD, knowledge base file upload, web link addition, and content search.
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Map the epistemic structure of a subject to determine knowledge types and inform curriculum sequencing. Use when designing courses, restructuring programmes, or analysing knowledge architecture.
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Intent-driven knowledge-store and Run knowledge lifecycle management — read-only audit/prune reports, stage candidates (with signal recording), review/resolve/promote candidates, harvest artifacts, or manage wiki/domain knowledge.
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Load project-specific context -- tech stack, architecture overview, directory layout, trusted sources, and conventions -- so that all skills operate with awareness of what this project actually is. Use when a knowledge base document exists, or when the user asks about the project's tech stack, architecture, conventions, framework, directory layout, or says 'tell me about this project', 'what are we using?', 'what's our stack?', or 'what framework is this?'. Use the knowledge-priming-refiner to create a knowledge base document.
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Builds a portable, embedding-free knowledgebase from a set of files and delivers it as a self-contained `.skill` bundle (BM25 index + bundled searcher + query protocol). Use when a user wants to turn uploaded files, a folder, or a corpus into a searchable knowledgebase they can hand to any agent — phrased as "make a knowledgebase", "build a KB skill", "package these docs for retrieval", "create a searchable bundle", or references to a `.skill` KB. The output runs anywhere with Node or Python — no model, no install, no network. Distinct from `bm25` (ephemeral in-session search) and `building-github-index` (markdown project-knowledge index).
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Map the epistemic structure of a subject to determine knowledge types and inform curriculum sequencing. Use when designing courses, restructuring programmes, or analysing knowledge architecture.
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Build and maintain a persistent, LLM-maintained markdown wiki following Andrej Karpathy's LLM Wiki pattern — the agent incrementally compiles raw sources into an interlinked knowledge base instead of re-deriving answers from scratch. Use this whenever the user wants to ingest a source (an article, paper, transcript, chapter, meeting notes), file knowledge or conversation findings into the wiki, query the accumulated knowledge base, lint or health-check the wiki, or mentions "the wiki", "knowledge base", "ingest this", "file this", "what do we know about X", or accumulating notes on a topic over time — even if they don't say "LLM Wiki" explicitly.
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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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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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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.
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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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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.
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Structured interview and observation techniques for extracting tacit knowledge from domain experts
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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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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. Use when building a knowledge base from many documents that should accumulate and cross-reference over time instead of being re-read each session.
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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.
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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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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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knowledge/raw/ の生の学びを分類・統合して knowledge/curated/ に書き出す。 ディスパッチャーが閾値超過で起動したキュレーターから呼ばれるほか、 「知見を整理して」と言われたときに使う。
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Structured interview and observation techniques for extracting tacit knowledge from domain experts
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Auto-generate knowledge YAML from the codebase. Use when the user runs /sync-knowledge or wants knowledge files refreshed.
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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/)
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Review and search project knowledge that gstack has collected across work sessions. Find past lessons, prune outdated entries, and export recorded knowledge when you need to revisit earlier solutions or organize what the project has learned.
Store facts, daily events, and user preferences in files that persist across sessions. Organize knowledge with PARA, a method separating projects, ongoing responsibilities, references, and archives, and retrieve saved information through qmd search.