Knowledge

Search all knowledge stores for: $ARGUMENTS

Workflow

Phase 1: Parallel Search (Observe)

Execute all searches in parallel:

1. MEMORY.md: Search the auto memory file at .claude/projects/*/memory/MEMORY.md for the query terms using Grep
2. Thoughtbox Knowledge Graph: Use ToolSearch to load thoughtbox_execute, then search entities and observations matching the query via tb.knowledge.listEntities({ name_pattern: "..." }) and tb.knowledge.queryGraph(...)
3. Git History: Run git log --all --oneline --grep="$ARGUMENTS" -20 for commit history
4. Assumption Registry: Search .assumptions/*.jsonl for matching assumption records using Grep
5. DGM Patterns: Search .dgm/fitness.json for patterns matching the query using Grep
6. Session Handoffs: Search .sessions/handoff-*.json for relevant context using Grep

Phase 2: Collate and Rank (Orient)

For each result found:

1. Note the source store (provenance)
2. Note the freshness (when was this last updated/verified)
3. Note the relevance (how closely does it match the query)
4. Check for cross-references (does this result reference other stores)

Phase 3: Present Results (Act)

Present results grouped by relevance, with provenance:

## Knowledge Query: "{query}"

### High Relevance
- [MEMORY.md] {finding} (line {N}, updated {date})
- [Thoughtbox] Entity: {name} — {observation} (created {date})

### Medium Relevance
- [Git] {commit-hash}: {message} ({date})

### Low Relevance
- [Assumptions] {assumption} (confidence: {N}%, last verified: {date})

### Cross-References
- Thoughtbox entity "{name}" relates to git commit {hash}

### Gaps
- No results found in: {store1}, {store2}
- Consider adding knowledge about "{query}" to {suggested_store}

Notes

• If a store doesn't exist yet (e.g., .dgm/fitness.json not created), skip it silently
• If Thoughtbox MCP tools aren't available, skip the knowledge graph search
• Always show which stores were searched and which returned nothing — gaps are informative
• If the query is broad, suggest more specific sub-queries