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Search your knowledge base when past decisions, preferences, or procedures would improve the response. Covers memories from every AI tool you use.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Use Nowledge Mem from WorkBuddy or CodeBuddy for startup context, memory search, durable saves, thread search, and WorkBuddy/CodeBuddy transcript import.
Read your daily Working Memory briefing to understand current context. Contains active focus areas, priorities, unresolved flags, and recent knowledge changes. Load this automatically at the beginning of sessions for cross-tool continuity.
Search memory store when past insights would improve response. Recognize when user's stored breakthroughs, decisions, or solutions are relevant. Search proactively based on context, not just explicit requests.
Use Nowledge Mem from Kimi Code for startup context, memory search, durable saves, thread search, and Kimi Code transcript import.
Check Nowledge Mem setup, detect your agent, and guide native connector setup. Use when the user asks about setup, configuration, or when memory tools aren't working as expected.
Save decisions, insights, preferences, and procedures as durable memories. Fires when the conversation produces knowledge worth keeping across sessions.
| name | search-memory |
| description | Search your knowledge base when past decisions, preferences, or procedures would improve the response. Covers memories from every AI tool you use. |
Strong signals:
Contextual signals:
Skip when:
nmem --json m search "3-7 word semantic query"
If the runtime already knows the active project or agent lane, add --space "<space name>".
When the user asks about a prior session, discussion, or exact exchange:
nmem --json t search "query" --limit 5
If a thread looks relevant, load it incrementally:
nmem --json t show <thread_id> --limit 8 --offset 0 --content-limit 1200
Increase --offset only when more messages are actually needed.
For continuation-heavy engineering work, search near the start of the task rather than waiting for an explicit recall request.
| Flag | Purpose |
|---|---|
--mode deep | Conceptual or weak first-pass results |
-l label | Filter by label (multiple uses AND logic) |
-n limit | Limit number of results (default: 10) |
--importance MIN | Minimum importance score (0.0-1.0) |
--time RANGE | Time filter: today, week, month, year |
# Semantic search with importance filter
nmem --json m search "database optimization" --importance 0.7
# Filter by labels
nmem --json m search "React patterns" -l frontend -l react
# Recent memories only
nmem --json m search "deployment fix" --time week -n 5
# Deep mode for conceptual queries
nmem --json m search "auth architecture rationale" --mode deep
Scores: 0.6-1.0 direct match. 0.3-0.6 related. Below 0.3, skip.
Found: Synthesize and cite when helpful. None: State clearly. Suggest distilling if the current discussion is valuable.
Do not search for every message. Search when there is a reasonable expectation that prior knowledge exists and would improve the response. One well-targeted search is better than three speculative ones.