7xuanlu/wenlan/recall
Claude Code and agent skill by 7xuanlu/wenlan
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Checking that the SKILL.md file is still in the repository.
Search Wenlan'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".
Install
npx vskill@latest install 7xuanlu/wenlan/recallSKILL.md
/recall
Search Wenlan's memory by natural-language query. Returns matching memories ranked by hybrid vector + FTS search, then re-ordered by the agent if it helps.
Resolve the active space
The /recall skill accepts one optional inline token of the form space:<name> anywhere in the argument string. The bundled resolver strips it and returns the rest as the query. It prints the resolved Space line itself:
If query is empty, ask the user what they want to search for. Pass space="$space" to the recall MCP tool only when space is non-empty; otherwise omit the space filter.
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
Inferences (do not ask the user):
space: current working directory (e.g.~/Repos/wenlan/...→"wenlan"),
the topic being discussed, or whatever space was mentioned in recent turns. Pass only when scope is known; omit when uncertain or unscoped.
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.
If the user explicitly asks how one returned memory changed, for its full history, or whether a correction was recorded, call get_memory_revisions(memory_id="<source_id>"). Do not fetch revision chains for ordinary recall results.
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:
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.
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingplugin-codex/skills/recall/SKILL.md
Popularity
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