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Get Started Free →Search Origin's local memory by query. Targeted lookup, not orientation. Invoked as `/recall <query>`. Use when the user asks "do you remember", "what do you know about", "look up".
.claude/skills/davepoon-recall/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-17 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-05 | ✓→✗ | ▼ Worse | 92% | 0% |
| case-06 | ✓→✗ | ▼ Worse | 425% | 0% |
| case-16 | ✓→✗ | ▼ Worse | -16% | 0% |
Search Origin's memory by natural-language query. Returns matching memories ranked by hybrid vector + FTS search, then re-ordered by the agent if it helps.
When a local model or API key is configured, the daemon can rerank and expand server-side. In local memory mode it cannot. The skill always does agent-side expansion and rerank itself — cheap, makes results good in both modes.
Before calling recall, rewrite the user's query into a more search-friendly form:
"auth" → "auth OR authentication").
Don't over-expand. If the query is already specific, leave it alone. One recall call per /recall invocation — duplicate calls double embedding load and the merge step is rarely worth it. The daemon's own search_memory_expanded exists for the multi-query case; if it matters, use that endpoint instead of issuing parallel calls here.
recall(query="<expanded query>", space=<inferred>, memory_type=<inferred>)Inferences (do not ask the user):
space: current working directory (e.g. ~/Repos/origin/... → "origin"),the topic being discussed, or whatever space was mentioned in recent turns. Always pass when scope is known; if uncertain, run list_spaces later (post-PR-C) or omit.
memory_type: only when the query itself names a type ("decision on X","lesson about Y", "preference for Z"). Otherwise omit and let hybrid search rank.
limit: default 10. Use 3-5 for quick lookups, 10-20 for exploration.The daemon returns hits ranked by hybrid search. That ranking is good but not perfect — it doesn't know the user's exact intent.
Re-read the returned memories against the original query. Promote the ones that directly answer the question; demote ones that just share keywords.
Show the user the top 3-5 reranked hits. Surface the rest only if asked.
Each memory may carry revision fields: version, pending_revision, merged_from, last_delta_summary. Most memories are fresh (v1, none set) — render nothing extra for those. Only add a tag line when something meaningful is present.
Condition: emit the tag line when any of these holds:
version > 1merged_from is non-emptypending_revision == trueFormat — one compact line above the memory body:
<id> v<N> (merged <K> memories) ← merged_from has K entries
<id> v<N>, pending revision against <id> ← pending_revision true
<id> v<N> — <last_delta_summary> ← version > 1, delta populated
<id> v<N> ← version > 1, no deltaRules:
— <delta> when last_delta_summary is empty or null.flag is set. Preserves current output for fresh memories.
/brief instead./capture."Alice database preference" finds more than "database stuff". The semantic matcher rewards specificity. If too many results return, add filters rather than making the query longer.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 4,487 | 4,294 | -4% | 1 | 1 | 0% | 755 | 1,202 | +59% | 0 | 0 | — |
case-02 | fail→fail | 14,196 | 6,402 | -55% | 1 | 1 | 0% | 2,702 | 1,395 | -48% | 0 | 0 | — |
case-03 | fail→fail | 6,083 | 6,011 | -1% | 1 | 1 | 0% | 1,009 | 1,344 | +33% | 0 | 0 | — |
case-04 | pass→pass | 10,277 | 4,841 | -53% | 1 | 1 | 0% | 1,541 | 1,867 | +21% | 0 | 0 | — |
case-05 | pass→fail | 8,569 | 13,714 | +60% | 1 | 1 | 0% | 1,014 | 1,945 | +92% | 0 | 0 | — |
case-06 | pass→fail | 2,155 | 12,814 | +495% | 1 | 1 | 0% | 378 | 1,984 | +425% | 0 | 0 | — |
case-07 | fail→fail | 6,106 | 9,114 | +49% | 1 | 1 | 0% | 1,043 | 1,411 | +35% | 0 | 0 | — |
case-08 | fail→fail | 11,394 | 6,037 | -47% | 1 | 1 | 0% | 1,859 | 1,327 | -29% | 0 | 0 | — |
case-09 | fail→fail | 6,839 | 4,353 | -36% | 1 | 1 | 0% | 1,172 | 1,179 | +1% | 0 | 0 | — |
case-10 | fail→fail | 4,569 | 5,178 | +13% | 1 | 1 | 0% | 675 | 1,193 | +77% | 0 | 0 | — |
case-11 | fail→fail | 12,108 | 5,520 | -54% | 1 | 1 | 0% | 1,954 | 1,301 | -33% | 0 | 0 | — |
case-12 | fail→fail | 4,689 | 5,236 | +12% | 1 | 1 | 0% | 706 | 1,308 | +85% | 0 | 0 | — |
case-13 | fail→fail | 10,230 | 5,137 | -50% | 1 | 1 | 0% | 2,139 | 1,247 | -42% | 0 | 0 | — |
case-14 | fail→fail | 4,322 | 5,468 | +27% | 1 | 1 | 0% | 781 | 1,178 | +51% | 0 | 0 | — |
case-15 | fail→fail | 11,705 | 5,274 | -55% | 1 | 1 | 0% | 1,944 | 1,299 | -33% | 0 | 0 | — |
case-16 | pass→fail | 13,651 | 29,657 | +117% | 1 | 1 | 0% | 1,531 | 1,280 | -16% | 0 | 0 | — |
case-17 | fail→pass | 6,488 | 3,863 | -40% | 1 | 1 | 0% | 1,298 | 1,814 | +40% | 0 | 0 | — |
case-18 | fail→pass | 5,539 | 3,202 | -42% | 1 | 1 | 0% | 1,066 | 1,598 | +50% | 0 | 0 | — |
case-19 | fail→fail | 5,000 | 2,545 | -49% | 1 | 1 | 0% | 941 | 1,421 | +51% | 0 | 0 | — |
case-20 | pass→fail | 4,209 | 2,078 | -51% | 1 | 1 | 0% | 792 | 1,370 | +73% | 0 | 0 | — |
case-21 | pass→fail | 2,649 | 2,063 | -22% | 1 | 1 | 0% | 548 | 1,303 | +138% | 0 | 0 | — |
case-22 | fail→fail | 9,864 | 5,593 | -43% | 1 | 1 | 0% | 1,839 | 1,336 | -27% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 6 counted toward the lift figure. The other 16 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -14 percentage points is the difference between those two pass rates over the 6 comparable cases. 7 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.