recall
GitHub通过自然语言查询检索本地记忆。包含查询重写、调用MCP工具及结果重排序三步流程，支持按空间/类型筛选，并根据版本信息渲染修订上下文，返回最相关的3-5条记忆。
触发场景
安装
npx skills add davepoon/buildwithclaude --skill recall -g -y
SKILL.md
Frontmatter
{
"name": "recall",
"description": "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\".\n",
"allowed-tools": [
"mcp__plugin_origin_origin__recall"
],
"argument-hint": "<query>"
}
/recall
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.
Two phases
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.
Phase 1 — expand the query (agent-side)
Before calling recall, rewrite the user's query into a more
search-friendly form:
- Replace pronouns with the referent ("it" → the actual thing).
- Expand abbreviations the embedder is unlikely to know.
- Add the obvious synonym when the original term is too narrow (e.g. "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.
Phase 2 — call the MCP tool
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, runlist_spaceslater (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.
Phase 3 — rerank (agent-side)
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.
Phase 4 — render revision context (per result)
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_fromis non-emptypending_revision == true
Format — 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 delta
Rules:
- Merged takes precedence over pending_revision in the label.
- Omit
— <delta>whenlast_delta_summaryis empty or null. - Skip the tag line entirely when version == 1 (or null) and no other flag is set. Preserves current output for fresh memories.
When to use
- "What did I say about X?"
- "Do you remember the decision on Y?"
- Need a specific fact before continuing.
When NOT to use
- Broad session orientation → use
/briefinstead. - Storing a new memory → use
/capture.
Hint: write specific queries
"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.
版本历史
- 502fc01 当前 2026-07-05 15:16
