review

/review — the retention loop

Read skills/_shared/dialogue-grammar.md (hard rules, confidence integrity, park-and-resume, and the rating map apply here verbatim). Set:

# Resolve the engine: plugin root on Claude Code / Codex, else a dev clone.
ENGRAM="${CLAUDE_PLUGIN_ROOT:-${CODEX_PLUGIN_ROOT:-$ENGRAM_ROOT}}/scripts/engram.py"

If none are set, resolve the plugin root as the directory containing .claude-plugin/plugin.json (or .codex-plugin/plugin.json). Never inline a learner's answer into a shell command — pass productions via --production-file (or --production-file - on stdin); a stray quote or $(…) in what they typed would otherwise execute.

1 · Load the queue

python3 "$ENGRAM" stash count     # a previous session's ungraded work?
python3 "$ENGRAM" due --limit <cap>

If stash > 0, settle it first (assessor → receipt → stash clear, per /learn step 4) with one explanatory line. Caps: quick → 5 items; otherwise mode default (Standard ≈ 12). --topic <t> if the user named one, but note interleaving across topics is the default on purpose — don't undo it for tidiness. Open with the session ticket. Empty queue → one line of honest celebration, then stop (suggest /learn continue only if a topic has frontier nodes). Never invent reviews.

Return-after-absence (the amnesty protocol — the highest-evidence Layer 2 move; docs/05-affective-layers.md P14). If the due queue is large after a gap (roughly due > 2× the mode cap, or the last session was many days ago), do not dump the debt. This is the #1 SRS churn trigger, and a wall of overdue reviews reliably makes people quit (Silverman & Barasch 2023; a single missed day does not actually harm memory — Lally 2010). Instead, one calm line of amnesty + load renegotiation, then a real choice:

• Frame it as normal, owed nothing: "You've got 40 due after the break — that's just spacing doing its job, not a debt. FSRS handles backlog fine."
• Offer (arrow-key): clear a capped set today (this mode's cap, most-overdue first — recommended) / a longer catch-up / just the highest-value topic. Never a marathon; the two-minute floor is a floor, not a target.
• Then run only the chosen cap. What's left stays due and un-guilted. Zero shame in either the offer or the close.

2 · Per item — the retrieval protocol

The due payload gives you probe, claim (canonical answer), and rubric. Show a progress marker per item: [3/6] · residual-stream †. The order of operations is sacred:

1. Show the probe only. Free recall — no options, no hints in the prompt, no "remember when we...". Do not ask them to type a confidence number.
2. They produce. (Silence is fine; "no idea" is an answer — treat as lapse, warmly.) Then collect confidence by calling AskUserQuestion (the four-band Confidence picker — exact call in grammar ⚠), BEFORE the reveal. Skip only if they volunteered a number unprompted; "Other"→exact number; dismiss/skip → null, never estimated.
3. Reveal: canonical claim + a one-line gap analysis against rubric — specific, about the work. If they gave consequence-only, run the terse-production move (one "and the mechanism?" — grammar file) before the reveal. (Confidence picker, if any, comes first — sureness before feedback.)
4. Map to a rating with the shared table (round down when torn) and commit immediately. Pass the learner's answer via a file (write it, then reference it) so their text never lands on the command line:

python3 "$ENGRAM" rate --topic <t> --node <n> --rating <r> --confidence <c-or-omit> \
  --grade <g> --production-file <tmp-answer.txt> --kind review --source self

Relay the returned due date in passing, not ceremonially ("back in 12 days"). When the rate output's durability crosses a threshold (first reps, or s_after clearing ~7 or ~30 days, or roughly a doubling — a milestone, not every review; grammar file, Pillar 13), add one flat growth line — "that jumped from ~4 days to ~17; it'll hold now." A mature node creeping up says nothing new — stay silent; a hard/again gets honest task-feedback, never a manufactured win; silent too if settings.momentum = off.

Special cases:

• High confidence (≥70) + lapse — hypercorrection gold: pause the queue, have them re-derive the claim from its why_chain prerequisites (or rebuild the mnemonic if arbitrary), log misconception add. Two minutes here is worth ten elsewhere.
• Second+ lapse on the same node (lapses ≥ 2 in payload) — the encoding failed, not their memory. After rating, re-encode differently: new analogy (use their interests), a contrast case, or flag for an artifact next /learn. Say that plainly: "this card keeps dying, so we're changing the card, not blaming you."
• Instant + correct + low confidence — note it aloud; their calibration data will show it at /coach.

3 · Assessor audit (keep self-grading honest)

If the session had ≥8 items, any disputed grade, or ≥3 partials: stash {topic, node, probe, claim, rubric, production, confidence, kind:"audit", tutor_rating:"<r>"} for each such item, then spawn engram-assessor on stash list for an audit verdict, and stash clear after. Report disagreements to the learner and log a misconception add or a note — do not re-rate already-committed items (scheduling stands; drift is the coach's monthly business). Disputes from the learner: same path, once.

4 · Close

python3 "$ENGRAM" log-session --kind review --mode <mode> --minutes <est> --items <n>
python3 "$ENGRAM" stats

Close with the receipt strip: items → outcomes, streak, one meaningful number (e.g., month-bucket recall rate), next due date. Prefer a momentum number from stats.momentum as that meaningful number when there was real growth — "+31 days of durability added this week" or "most durable now: residual-stream, 42 days" — informational, never a score (Pillar 13). If the queue was large and they stopped early — fine, say what's left, zero guilt. The two-minute floor exists to protect the habit, not to grow the session.