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/recall

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".

From plugin
wenlan
9421 skills3 agents1 MCP

Claude Code

$ npx -y skills add wenlan-app/wenlan --skill recall --agent claude-code

SKILL.md

recall.SKILL.md
name: recall
description: >
  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".
argument-hint: "<query>"
allowed-tools: ["Bash", "mcp__wenlan__recall", "mcp__wenlan__get_memory_revisions"]
user-invocable: true

/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:

raw_args="<the full argument string passed to /recall>"
# The resolver ships inside the installed plugin; the relative path only
# exists in a wenlan checkout.
resolver="$(find "$HOME/.codex/plugins/cache" -path '*/wenlan/*/bin/resolve-space.sh' 2>/dev/null | head -1)"
resolved="$("${resolver:-plugin-codex/bin/resolve-space.sh}" --cwd "$PWD" --args "$raw_args")"
space="$(printf '%s\n' "$resolved" | cut -f1)"
source_layer="$(printf '%s\n' "$resolved" | cut -f2)"
query="$(printf '%s\n' "$resolved" | cut -f3-)"

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

recall(query="<expanded query>", space=<resolved if non-empty>, memory_type=<inferred>)

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 > 1`
  • `merged_from` is non-empty
  • `pending_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>` when `last_delta_summary` is 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 `/brief` instead.
  • 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.

Read more
@7xuanlu
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Open-source Obsidian alternative that works with your existing vault. The knowledge base you don't have to maintain: your AI tools keep it organized and up to date, with no API keys or plugins to set up. For you and your team.

9421 skills3 agents1 MCP
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Questions about recall.

Which coding agents can use recall?
recall installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.