Cross-Session Memory Recall
Announce: "I'm using enggenie:memory-recall to search previous session context."
Overview
Search past work across sessions using claude-mem's MCP tools. Token-efficient 3-layer retrieval: search the index first, filter, then fetch only what matters.
Requires: claude-mem plugin installed. If not installed:
- When invoked by other skills → skip silently, proceed without memory
- When invoked directly by user → show: "enggenie:memory-recall requires the claude-mem plugin. Install with:
claude plugin add claude-mem"
3-Layer Workflow
Never fetch full details without filtering first. 10x token savings.
Layer 1: Search - Get Index with IDs
search(query="authentication", limit=20, project="my-project")Returns a lightweight table (~50-100 tokens per result):
| ID | Time | T | Title | Read |
|----|------|---|-------|------|
| #11131 | 3:48 PM | feature | Added JWT authentication | ~75 |
| #10942 | 2:15 PM | bugfix | Fixed auth token expiration | ~50 |Parameters:
query(string) - Search termlimit(number) - Max results, default 20, max 100project(string) - Project name filtertype(string, optional) - "observations", "sessions", or "prompts"obs_type(string, optional) - Comma-separated: bugfix, feature, decision, discovery, changedateStart/dateEnd(string, optional) - YYYY-MM-DD or epoch msoffset(number, optional) - Skip N resultsorderBy(string, optional) - "date_desc" (default), "date_asc", "relevance"
Layer 2: Timeline - Get Context Around Interesting Results
timeline(anchor=11131, depth_before=3, depth_after=3, project="my-project")Or find anchor automatically:
timeline(query="authentication", depth_before=3, depth_after=3, project="my-project")Returns chronologically ordered items around the anchor point.
Layer 3: Fetch - Get Full Details ONLY for Filtered IDs
Review titles from Layer 1 and context from Layer 2. Pick relevant IDs. Discard the rest.
get_observations(ids=[11131, 10942])Returns complete observation objects (~500-1000 tokens each): title, subtitle, narrative, facts, concepts, files.
Always use get_observations for 2+ observations - single request vs N requests.
Code Exploration (AST-Based)
When exploring code structure, use token-efficient AST tools instead of full file reads:
- smart_search - Find symbols across codebase (~2-6k tokens vs ~39-59k for Explore agent)
- smart_outline - Get file structure (~1-2k tokens vs ~12k+ for full Read)
- smart_unfold - See specific function implementation (~400-2k tokens)
How Other Skills Use Memory
Every skill that references memory does this:
IF memory-recall MCP tools available:
Search for relevant context
Use findings in skill logic
ELSE:
Skip silently - proceed without memory
No error message, no mention of missing featureSkills that use memory:
- enggenie:architect-design - "Have we designed something similar?"
- enggenie:architect-plan - "What patterns did we use last time?"
- enggenie:pm-refine - "Have we built something similar?"
- enggenie:dev-debug - "Have we seen this bug pattern before?"
Token Savings
| Operation | Without memory-recall | With memory-recall |
|---|---|---|
| Find past work | Read full conversation logs | Search index: ~100 tokens/result |
| Explore code | Explore agent: ~39-59k tokens | smart_search: ~2-6k tokens |
| Read file structure | Full Read: ~12k+ tokens | smart_outline: ~1-2k tokens |
| See one function | Read full file: ~5-10k tokens | smart_unfold: ~400-2k tokens |
Examples
Find recent bug fixes:
search(query="bug", type="observations", obs_type="bugfix", limit=20, project="my-project")Find what happened last week:
search(type="observations", dateStart="2025-11-11", limit=20, project="my-project")Understand context around a discovery:
timeline(anchor=11131, depth_before=5, depth_after=5, project="my-project")Batch fetch details:
get_observations(ids=[11131, 10942, 10855], orderBy="date_desc")When Search Returns No Results
- Broaden the query - try synonyms, related terms, or the feature area instead of specific implementation details
- Check recent sessions - use timeline with a recent anchor to see what was discussed recently
- Skip gracefully - if no relevant results after 2 search attempts, proceed without memory context. Say: "No relevant past context found. Proceeding fresh."
Do not spend more than 2 search attempts. Memory is a shortcut, not a requirement.
Staleness
Memory observations are snapshots in time. Before acting on a recalled decision:
- Architecture decisions (ADRs, tech choices): Verify the decision is still in effect. Check if the code reflects it.
- Bug patterns ("we fixed this by..."): Check if the same fix applies. The codebase may have changed.
- Process decisions ("we agreed to..."): Ask the user if this is still the team convention.
When in doubt, treat memory as a starting point for investigation, not as ground truth.
Recommended Model
Primary: haiku Why: Memory retrieval is about searching indexes and fetching observations. Haiku is fast and efficient for this lookup-heavy work.
This is a recommendation. Ask the user: "Confirm model selection or override?" Do not proceed until the user responds.
Entry Condition
None - available anytime. Invoked by other skills as a utility, or directly by user.
Exit Action
Context provided → resume whatever workflow triggered the recall.