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Deployment tool and support utility for AI context. Copies agents, skills, commands, rules, and behaviors into the paths each AI platform reads (Claude Code, Codex, Copilot, Cursor, Warp, OpenClaw, and 6 more) so one source of truth works across 10 platfo

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--- name: knowledge-base-quickref namespace: aiwg platforms: [all] kernel: true description: AUTO-INVOKE when user mentions knowledge base, wiki, KB, semantic memory, llm-wiki, knowledge ingest, document corpus. Knowledge-base framework quick reference — discovery phrases for KB ingest/health, semantic-memory kernel skills, llm-wiki profiles. --- # Knowledge Base Framework — Quick Reference This is your always-loaded directory for the AIWG **knowledge-base** framework. It does **not** list every skill. Most heavy lifting comes from the **semantic-memory kernel** in `aiwg-utils` (`memory-ingest`, `memory-lint`, etc.) — this framework is a thin topology on top. ## Canonical access pattern: discover → show When you find a candidate via `aiwg discover`, fetch its body with `aiwg show <type> <name>`. **Never** use `find`, `ls`, `Glob`, or direct `Read` on `<provider>/skills/` paths — those reflect the kernel-pivot deploy state, not the full surface. ```bash aiwg discover "<phrase>" # find — returns ranked candidates aiwg show skill <name> # fetch — streams the SKILL.md body ``` If your platform's Skill tool errors on a non-kernel skill (expected — most aren't kernel), the fallback is `aiwg show`, never filesystem browsing. Last-resort if `aiwg` itself is broken: read directly from `$AIWG_ROOT/agentic/code/...` (the canonical corpus, always present). ## How to use this quickref 1. Identify the **capability domain** the user's need belongs to 2. Pick a **curated phrase** from that domain 3. Run `aiwg discover "<phrase>"` and surface the top match to the user **Do not enumerate skills from memory.** Discovery is the lookup surface. ## What this framework is for A **thin topology** on top of AIWG's semantic-memory kernel — turning any project's `.aiwg/kb/` into a queryable knowledge base. Sources get ingested into structured pages (entities, concepts, summaries, syntheses) with cross-references, deduplication, and lint coverage. Pairs naturally with the `llm-wiki` addon for Obsidian-compatible profiles (book-companion / personal / research-deep-dive / business-team / generic). ## Capability domains | Domain | Covers | |---|---| | **KB lifecycle** | Ingest sources, health-check the KB | | **Semantic memory kernel** (in aiwg-utils) | Generic ingest/lint/log/query primitives any consumer can declare a topology against | | **LLM-wiki profiles** | Topology profiles that shape how `kb-ingest` derives pages | | **Cross-ref traversal** | Graph-native via `aiwg index neighbors --graph kb` | ## Curated discovery phrases ### KB lifecycle ```bash aiwg discover "kb-ingest" # → kb-ingest (score 1.00) aiwg discover "ingest source into knowledge base" # → kb-ingest aiwg discover "kb-health" # → kb-health (score 1.00) aiwg discover "knowledge base lint" # → kb-health ``` ### Semantic memory kernel (aiwg-utils) ```bash aiwg discover "memory ingest" # → memory-ingest aiwg discover "memory lint" # → memory-lint aiwg discover "memory log append" # → memory-log-append aiwg discover "memory log render" # → memory-log-render aiwg discover "memory query capture" # → memory-query-capture ``` ### LLM-wiki profiles (in the llm-wiki addon) ```bash aiwg discover "llm wiki profile" # → llm-wiki addon entries aiwg discover "book companion knowledge base" # → llm-wiki book-companion profile aiwg discover "research deep dive wiki" # → llm-wiki research-deep-dive profile ``` ### Cross-ref traversal (uses the artifact index, not a skill) ```bash aiwg index neighbors --graph kb --node <slug> # traverse the KB graph ``` ## How knowledge-base composes with semantic-memory ``` kb-ingest ─────┐ ┌──── memory-ingest (kernel) ├── declares topology ──┤ kb-health ─────┘ └──── memory-lint (kernel) memory-query-capture memory-log-append / render ``` Every KB entry is a semantic-memory entry with a KB-specific topology (page types, cross-ref style, derived-pages config). The kernel handles ingest mechanics; this framework declares *what shape* the KB takes. ## Page types When ingesting via `kb-ingest`, the topology produces: - **Entity pages** — people / orgs / products / works (one per noun) - **Concept pages** — ideas / methods / principles - **Source summaries** — per-source distillation (one per ingested URL/file) - **Synthesis pages** — composite views across multiple sources Cross-references between these are graph-native (visible to `aiwg index neighbors`). ## Profile selection (via `llm-wiki` addon) | Profile | Use for | |---|---| | `book-companion` | Reading a book, building a structured companion | | `personal` | Personal knowledge / journal-of-ideas | | `research-deep-dive` | Academic research project (uses research-corpus conventions) | | `business-team` | Team-shared business KB | | `generic` | No profile chosen — vanilla semantic-memory shape | Install via `aiwg use llm-wiki --profile <name>`. The profile shapes how `kb-ingest` derives pages. ## Artifact directory layout ``` .aiwg/kb/ ├── entities/ # Entity pages (PROF-* compatible if research-corpus also installed) ├── concepts/ # Concept pages ├── summaries/ # Per-source distillation ├── syntheses/ # Composite views └── log.jsonl # Semantic-memory event log ``` ## When the curated phrases don't fit ```bash aiwg discover "<your need, paraphrased>" --limit 5 ``` ## Anti-pattern: don't enumerate If a user asks "what KB skills are available?", **do not list from this skill**. Run: ```bash aiwg discover --type skill --limit 20 "<their interest area>" ```