| name | knowledge-agent |
| description | Answer questions using accumulated project memory as a knowledge base. Use when the user asks "why did we choose X", "what's our approach to Y", "what do we know about Z", "explain how our auth works", "what's the deployment process", "what libraries are we using", "remind me why we did X this way", or any question that has likely been answered or decided in a previous session. Also trigger when making architectural decisions to check if a similar decision was already made.
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knowledge-agent: Memory as Knowledge Base
Your memory DB contains decisions, insights, and notes from past sessions.
Before answering questions from scratch, search it — you may have already
figured this out.
Query Workflow
COWORK_MEM_DB=~/mnt/.claude/.cowork-mem/memory.db \
python3 {SKILL_DIR}/scripts/vector_search.py "<question>" --limit 8
python3 {SKILL_DIR}/scripts/memory_store.py search "<key term>" --limit 5
python3 {SKILL_DIR}/scripts/memory_store.py get <id1> <id2>
Answering from Memory
After retrieving relevant observations:
- Synthesize, don't quote — distill what the observations say into a direct answer
- Cite the age — "We decided this 3 days ago" gives the user confidence in freshness
- Flag uncertainty — if observations are old or sparse, say so
- Update if stale — if the situation has clearly changed, save a new observation
When Memory Doesn't Know
If search returns nothing relevant:
- Answer from your general knowledge or by reading the codebase
- If you derive something new and useful, save it:
python3 {SKILL_DIR}/scripts/memory_store.py add insight \
"<what you learned>" --tags "<relevant,tags>"
Decision Archaeology
When the user asks "why did we do X this way?":
python3 {SKILL_DIR}/scripts/memory_store.py search "<X>" --type decision --limit 10
If you find a past decision, explain it with context. If you don't, note that
"this decision isn't in memory — here's what I'd infer from the codebase."
