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Found 28 Skills
Route explicit LLM Wiki capture, ingest, integration, or bootstrap work.
Technical Document Knowledge Base (LLM Wiki) for Alibaba Cloud Tongyi Qianfan Platform. Activated when users inquire about Qianfan-related issues such as model lists, API parameters, error codes, application development (Agent/RAG/Knowledge Base/Memory/Plugins), model comparison and pricing, SDK/OpenAI compatible interfaces, multimodal capabilities (speech/image/video), Token billing, etc. It includes structured model market data in models (including contextWindow/QPM/pricing/sample code), wiki synthesis layer (topic pages/concept pages/comparison pages), and raw original document layer; for model specification issues, check models/index.md first, and for document-related issues, check wiki/index.md first.
Compiles and extracts session knowledge into a living, interconnected LLM-Wiki. Instead of writing isolated logs, it identifies key entities, updates cross-referenced topic files in docs/knowledgelib/, and maintains an index and chronological log. Use this to ensure persistent, compounding project knowledge.
Use when a Head of Ops, Knowledge Manager, or TPM-Internal needs to author, validate, or clean up company SOPs and internal runbooks (procurement intake, vendor offboarding, incident-comms cascade, employee onboarding, expense reimbursement, system-access provisioning, customer-escalation playbook) — including 5W2H completeness checks (Who-What-When-Where-Why-How-HowMuch), cross-link and orphan-page validation across a sprawling Notion/Confluence/Obsidian wiki, KB ingestion + hygiene reporting, ops onboarding doc generation, and runbook step verification (named owner, expected duration, observable success signal, rollback path, escalation contact). Pairs Kaoru Ishikawa's 5W2H method, Atul Gawande's *The Checklist Manifesto*, ISO 9001, ITIL v4 Service Operation, FDA 21 CFR Part 211, and Google SRE Workbook runbook discipline with deterministic stdlib-only Python tools that score completeness, detect anti-patterns, and emit prioritized cleanup lists. Distinct from `engineering/llm-wiki` (Karpathy-style personal PKM second brain), `engineering-team/runbook-generator` (system-ops production debugging runbook), `project-management/*` (Jira/Confluence delivery + ticket tracking), and sibling `business-operations/process-mapper` (BPMN process *design*, while knowledge-ops is process *documentation*).
Route durable graph-building requests into one honest mode: assistant-native install, local Python build, incremental refresh, graph query follow-up, or a graphify-style structural fallback for markdown-heavy corpora. Use when the user wants `GRAPH_REPORT.md`, `graph.json`, `graph.html`, repo/corpus relationship tracing, mixed code+docs+asset graphing, or graph-backed architecture understanding that should persist across sessions. Route simple locate/reference work to `codebase-search`, narrative knowledge-base work to `llm-wiki`, and project-memory handoff to `opencontext`.
Official skill for Youdao Cloud Note, supporting basic operations such as note CRUD (Create/Read/Update/Delete), to-do management, web clipping, note search, and folder management. If you need to build a knowledge base or Wiki, please use the youdaonote-llm-wiki skill instead of this skill.
Use this skill when users need to perform cross-wiki operations on knowledge content across multiple local LLM wikis (yzr-llm-wiki-management system) — from a global workspace perspective: cross-wiki scanning and indexing (INDEX/STATS), cross-wiki Q&A (routing/synthesis/comparison), cross-wiki cross-referencing and duplicate entity governance, workspace-level health checks (LINT), cross-wiki memory (MEMORY/), and workspace format upgrade. Triggers: "Summarize content about X across all my wikis" / "Compare what wiki A and B say about Y" / "Which wiki should I check for this question" / "Scan my workspace" / "Count the number of pages across all wikis" / "Workspace health check" / "Entity X in wiki A also exists in wiki B, add a link" / "Upgrade workspace". Use this skill whenever you need to read, summarize, compare or govern content across multiple wikis — even if the workspace or skill name is not explicitly mentioned. Not applicable: Operations within a single wiki (use yzr-llm-wiki-management); Single llmw command operations such as workspace/wiki metadata configuration, adding/removing wikis, session start/stop (run llmw directly, see llmw --help, no need to load this skill); Cloud collaborative wikis (Notion / Confluence / Outline, etc.).
Set up a new Obsidian knowledge base with the LLM Wiki pattern. Use when the user wants to create a second brain, initialize a vault, set up a personal knowledge base, or says "onboard". Guides through an interactive wizard to configure vault name, location, domain, agent support, and tooling.
Autonomously audit an LLM wiki (Karpathy pattern) for gaps, contradictions, orphans, and stale data, then research and fill high-priority gaps using quality-gated web research. Supports audit-only dry-run mode. Operates on a dedicated branch and commits changes for human review — never auto-merges. Use when the user asks to "lint my wiki", "self-heal my knowledge base", "find gaps in my wiki", "update my second brain", "auto-research my wiki", "run a health check on my LLM wiki", "audit my wiki without making changes", "dry run the lint", or wants to schedule periodic wiki maintenance.
Set up a new Obsidian knowledge base with the LLM Wiki pattern. Use when the user wants to create a wiki, second brain, personal knowledge base, initialize a vault, or says "onboard", "set up", "new wiki", or "new vault".
Karpathy LLM Wiki 패턴 기반 지식 관리 스킬. 코드 프로젝트와 옵시디언 노트 모두 지원. Raw Source(코드·문서)를 읽어 docs/wiki/에 누적형 지식베이스를 구축·유지한다. "wiki", "위키", "ingest", "인제스트", "wiki 점검", "wiki lint", "wiki 업데이트", "문서화해줘", "아키텍처 설명해줘", "어떻게 동작해?" 키워드로 트리거. qmd 검색 도구와 연동하여 토큰 절약 + 높은 검색 정확도 제공.
Portable OKF knowledge bundle for coding-agent memory and LLM wikis. Use when the user wants to start a wiki, ingest a source, document a repository, query project knowledge with citations, lint for drift, or visualize connections; whenever a repo has a knowledge/ bundle that should inform the task; and when another kb-* skill needs the shared spec, glossary, templates, or trust model.