/ijfw-auto-memorize

name: ijfw-auto-memorize
description: "Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory. Fires at session end. Requires consent on first run."

Fires at session end. Reads deterministic signals captured during the session and synthesizes structured memories. Nothing leaves the machine unless the user explicitly configured an API model via `IJFW_AUTOMEM_MODEL`.

Consent gate (first run only)

Before any synthesis, check `.ijfw/.automem-consent`:

• If missing: ask the user once: *"IJFW can automatically extract lessons (errors hit, fixes applied, preferences you stated) at session end into local memory. OK? (y/n). Reply `y`, `n`, or `ask` (ask again next time)."* Write answer as `{"consented": true|false, "at": "<iso>"}` to `.ijfw/.automem-consent`.
• If `"consented": false`: do nothing this session.
• If `"consented": true`: proceed.

Inputs (all local files)

• `.ijfw/.session-signals.jsonl` -- ERROR/FAIL/Traceback lines captured by the PreToolUse hook (W3.6).
• `.ijfw/.session-feedback.jsonl` -- corrections/confirmations/preferences detected by the UserPromptSubmit hook (W3.7).
• `.ijfw/.prompt-check-state` -- last turn's intent + vague signals.
• `.ijfw/memory/project-journal.md` -- existing entries (dedupe against these).
• Transcript read via Claude Code's Stop-hook payload (`transcript_path`).

Synthesis

For each signal cluster:

1. **Redact secrets first.** Call `redactSecrets()` from `mcp-server/src/redactor.js` on every field that came from transcript or tool output. 2. **Cap sizes.** Run `applyCaps` from `mcp-server/src/caps.js`. content ≤4KB, why/how ≤1KB, summary ≤120. 3. **Dedupe.** Use BM25 search (`mcp-server/src/search-bm25.js`) against `project-journal.md`. If score > 6 against an existing entry, skip (duplicate). 4. **Classify** into one of:

• `pattern` -- error→fix recurrence (same error type seen >=2x).
• `decision` -- an explicit user choice ("from now on X").
• `preference` -- a style/workflow preference ("I prefer Y").
• `observation` -- something worth noting, single instance.

5. **Emit** via `ijfw_memory_store` MCP tool with fields:

• `type`: one of the above
• `summary`: single sentence, ≤120 chars
• `content`: the fact + minimal context
• `why`: where this came from (e.g., "user said 'don't use X'", or "hit error Y at step Z")
• `how_to_apply`: when this should surface in future sessions
• `tags`: include `auto-memorize` and the classifier kind (`correction`, `confirmation`, `preference`, `rule`, `error`)

Model routing

`IJFW_AUTOMEM_MODEL` env var controls synthesis:

• unset or `off` -- skip LLM synthesis; only deterministic signals promoted 1:1.
• `claude-haiku-4-5-*` -- Anthropic Haiku (~$0.001/session).
• `ollama:<model>` -- local Ollama, fully offline.

Default ship: unset. Deterministic signals still become memories; only the richer "what did I learn" synthesis is gated on an LLM budget.

Output to user

One-line summary in the terminal: > *Stored 3 new memories: pagination-off-by-one fix, user prefers esbuild, stopped repeating rm -rf warnings.*

No summary on zero-emit sessions.

Audit trail

Every auto-stored entry carries `tags: [..., "auto-memorize"]`. The `/ijfw memory audit` command lists recent auto-entries for review/removal.

Safety

• **Never** store raw transcript content -- only redacted + capped extracts.
• **Never** call out to an LLM unless `IJFW_AUTOMEM_MODEL` is set AND consent is `true`.
• **Never** store secrets -- the redactor runs first, always.
• **Never** silently overwrite user-authored memories -- auto-entries go into the knowledge file with their distinguishing tag.

Resume normal mode after.