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knowledge-base-quickref

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.

Stars
141
Source
jmagly/aiwg
Updated
2026-05-31
Slug
jmagly--aiwg--knowledge-base-quickref
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/jmagly/aiwg/HEAD/agentic/code/frameworks/knowledge-base/skills/knowledge-base-quickref/SKILL.md -o .claude/skills/knowledge-base-quickref.md

Drops the SKILL.md into .claude/skills/knowledge-base-quickref.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

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.

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

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)

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)

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)

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

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:

aiwg discover --type skill --limit 20 "<their interest area>"