recall

name	recall
description	Workspace-wide semantic recall. Searches the local associative-memory index (scripts/memory-index.py: bge-m3 hybrid dense+BM25, on-machine, zero API cost) across everything meaningful in the workspace -- Odin brain, business threads, CRM, LinkedIn, context, reference, plans, deliverable outputs, knowledge, datastore extracts, and the auto-memory files -- then answers ONLY from the retrieved sources with file-path citations, or says "not in memory" when the index reports a gap. Use when the user asks "what do we know about X", "where did we decide Y", "have we touched Z before", "recall ...", "search my memory for ...", "find what we said about ...", or wants to surface a past decision / brief / contact / note by meaning rather than exact words. Do NOT use for: Odin-brain-only advice or episode dedup (use /odin recall, which is brain-scoped); external/world intelligence on a company or person (use /osint); capturing a NEW note (use /zk); plain exact-string file search (use Grep). This skill never fabricates beyond returned sources and never sends anything. CEO-only.
argument-hint	<what to recall> [--collection content|code|all] [--layer NAME] [--personal]
allowed-tools	Read, Bash(python3:*), Bash(python:*)
metadata	{"author":"Misha Hanin","email":"[e-mail]","version":"1.0"}
x-heading-orchestration	{"parallel_safe":"partial","shared_state":["auto-memory/"],"triggers":["recall","what do we know about","where did we decide","search my memory for","have we touched","find what we said about"]}
x-heading-capability	{"what":"Workspace-wide semantic recall - finds past decisions, briefs, contacts, and notes by MEANING (cross-lingual RU/EN) across the whole workspace, and answers only from retrieved sources with path citations, or says \"not in memory\".\n","how":"Run /recall <query>. It refreshes the index (incremental), queries scripts/memory-index.py with --json, reads the top cited files, and composes a cited answer. --collection code searches skills/rules; default is content.\n","when":"Use to surface something the workspace already knows. For Odin-brain advice use /odin recall; for external intel use /osint; to capture a new note use /zk; for exact-string search use Grep.\n"}
x-heading-routing	{"category":"Strategy","triggers":["recall","what do we know about","where did we decide","search my memory for","have we touched [X] before","find what we said about","surface past notes on [X]"],"exclusions":["Odin-brain-only advice / episode dedup -> /odin recall (brain-scoped)","external/world intel on a company or person -> /osint","capture a NEW note -> /zk","exact-string file search -> Grep. CEO-only, not synced to execs."],"compound":"No","router":"auto"}

Recall (workspace-wide semantic memory)

Surface what the workspace already knows, by meaning. The engine (scripts/memory-index.py) does hybrid dense+lexical retrieval entirely on the local machine (ollama bge-m3, zero API cost) across every meaningful layer. This skill turns its ranked hits into a cited answer — or relays its honest "gap" when the answer is not in memory. It never guesses past the sources, and it never sends anything.

CEO-only. Not synced to executives.

Phase 0 — Refresh, then query

1. Refresh the index first (mirrors /odin recall): run

   python3 scripts/memory-index.py build
   

   This is incremental — it embeds only changed files and updates the gitignored .memory-index/ cache. It is NOT a workspace write. If ollama is down, the build prints an embedding error; do not fail — note "index not refreshed (ollama down), recalling from the existing index" in one line and continue to the query against whatever is already indexed.

2. Query with JSON output:

   python3 scripts/memory-index.py query "<the user's question, RU or EN>" --json
   

   • Default collection is content (what we know / decided). To search the machinery (skills, rules), pass --collection code. To search everything, --collection all. To pin one layer, --layer NAME (e.g. odin, thread, crm, outputs, context, skill).
   • Pass the user's phrasing as the query text; the engine is cross-lingual, so a Russian question recalls English notes and vice-versa.

Phase 1 — Answer from sources, or admit the gap

Parse the JSON. It is one object:

• Gap ({"hits": [], "gap": true, "best": <float>, "threshold": <float>}): there is no match above the salience threshold. Say so plainly — e.g. "Not in memory: nothing above the recall threshold for that (closest match scored {best} vs {threshold})." Do NOT pad, do NOT speculate, do NOT answer from your own prior knowledge. Optionally name the nearest layer or suggest a rephrase or --collection all. Stop here.

• Hits ({"hits": [ {path,title,layer,ntype,classification,collection,score,channels}, ... ], "gap": false}):

  1. Read the top cited files (Read each path, highest score first — usually the top 3–5 are enough). Read the actual files; the JSON carries only titles and scores, not full content.

  2. Compose a concise answer grounded ONLY in what those files say. Every claim traces to a source. Cite inline as `path` after the claim it supports. If two sources disagree, surface the disagreement rather than silently picking one.

  3. If the read files do not actually contain the answer (a near-miss retrieval), say that honestly — "the closest sources touch the topic but don't answer it directly" — and name what they do cover. Never invent the missing fact.

  4. Reinforce cited auto-memory hits. For each cited hit whose layer is memory (auto-memory facts only — every other layer: odin, thread, crm, context, reference, plans, outputs, knowledge, datastore-extract, chronicle, skill, rule, is left untouched), once per unique file path, run:

     python3 scripts/memory-touch.py <path>
     

     This bumps access_count/last_accessed in the file's frontmatter only — it never touches content. Run this regardless of whether Phase 0's index build was a no-op (it edits the source file directly, not the index).

Phase 1.5 — Chronicle (historical class, always below the brain)

After the primary answer, run a SEPARATE secondary pass over the Conversation Chronicle — a dated record of past sessions ("on date X we discussed Y"). It is NOT a belief and NOT current fact; it only tells you a conversation happened.

python3 scripts/memory-index.py query "<the user's question>" --collection chronicle --json

• Append chronicle hits below the brain/content answer, never mixed into it. The separate collection is what makes this ordering structural: a brain hit always precedes a chronicle hit on the same topic. If the primary pass already answered, chronicle is supplementary "we talked about this on " context.
• Render each hit tagged with its date and class, e.g. [chronicle 2026-05-19] session about the Globex pricing debate — path``. Never restate a chronicle summary as a current decision; if it matters now, say "we discussed this on ; confirm whether it still holds."
• The chronicle's personal entries are air-gapped (personal segment) and never appear here — that is intended, not a miss.
• Skip this pass entirely when the user pinned --collection code/--layer, or when the chronicle query returns a gap (say nothing rather than pad).

Phase 1.6 — Personal chronicle (EXPLICIT opt-in ONLY)

Personal-life sessions are air-gapped: the personal path segment is a hard-coded deny, so personal chronicle is in NO persistent index and is NEVER part of the default recall, Phase 1.5, --collection all, or auto-inject.

Run a personal-chronicle search only when the user explicitly asks for it — recall --personal ..., "search my personal history", "when did I discuss the villa", etc. Never on a normal recall.

python3 scripts/chronicle.py personal-recall "<the user's question>"

This reads chronicle/personal/*.md ON THE FLY, scores locally (bge-m3, lexical fallback), and persists NOTHING. Present the dated hits tagged [Личное <date>], clearly historical, never as a current fact. If it returns no match, say so plainly. Do NOT run this pass in the same breath as an outbound draft unless the user asked for it — the whole point of the wall is to keep personal life out of send-capable contexts unless summoned.

Phase 2 — Source list

End with a one-line-per-source list of what you cited, each as a clickable path, with its layer and classification tag, so the CEO can open the originals. Example:

Sources:
- knowledge/odin-brain/positions/20260318140300-valuation-path-billion.md  (odin, ceo-only)
- threads/business/2026-05-19-globex-systems-engagement.md  (thread, ceo-only)

Voice

• Match reference/misha-voice.md and the always-on humanisation rule. Plain, committed, specific. No "I cannot find information regarding..." filler — either answer with citations or state the gap in one clean line.
• Hyphens, not double dashes. ODUN.ONE, DPI+, Tribe per terminology.

NEVER

• Never fabricate beyond the returned sources. No answer from your own training knowledge when the engine reports a gap — relay the gap.
• Never send anything. This is a read/recall skill; it drafts no outbound message and calls no send transport.
• Never read the vault or the personal thread branch. The engine air-gaps them structurally (_secure/ prefix, personal segment); do not work around it by reading those paths directly.
• Never present a hit's snippet as the answer without reading the file. The index stores a 500-char embed snippet, not the full note.
• Never claim freshness you don't have. If ollama was down and the index wasn't refreshed, say so.
• Never treat a chronicle hit as current fact or a decision. It is a dated historical record of a past conversation; surface it tagged with its date, below the brain, and flag that it may be stale.
• The reinforcement touch step never modifies content. scripts/memory-touch.py only bumps access_count/last_accessed in frontmatter, scoped to memory-layer hits; it refuses any path outside the auto-memory directory.