Spaced-repetition review sessions and retention tracking for subjects learned with the agent-tutor skill. Recall-first practice over lesson notes, adaptive FSRS-style interval scheduling (uncapped by default), honest retention verdicts, and a mixed mini-quiz per session. Use when the user wants to revise or practice old topics, check what they remember, or when the dashboard shows notes up for review.
Scanned 9/5/2026
Install to Claude Code
Skills Directorynpx -y skills add Mohamed-El-Sharqawy/agent-tutor --skill agent-tutor-review --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: agent-tutor-review
version: 3
description: Spaced-repetition review sessions and retention tracking for subjects learned with the agent-tutor skill. Recall-first practice over lesson notes, adaptive FSRS-style interval scheduling (uncapped by default), honest retention verdicts, and a mixed mini-quiz per session. Use when the user wants to revise or practice old topics, check what they remember, or when the dashboard shows notes up for review.
license: MIT
---
# Agent Tutor — Review
Understanding decays. This skill runs recall-first review sessions and keeps the retention schedule honest.
**Vault root:** `OBSIDIAN_VAULT` env var, else `learning/` in the current workspace. All content under `<vault>/Learning/`.
## The schedule
Every lesson note carries a `review:` block in its frontmatter with the note's memory state:
```yaml
review:
interval: 6 # current interval in days
ease: 2.5 # multiplier applied on a solid recall
due: 2026-08-28 # next review date
```
Scheduling is **FSRS-inspired and adaptive** — no fixed ladder and no ceiling. After each review, compute the next state:
| Verdict | Interval | Ease |
|---|---|---|
| **Solid** | `round(interval × ease)` | `+0.05`, up to `max_ease` |
| **Shaky but recoverable** | `round(interval × 1.2)` | `−0.2`, down to `min_ease` |
| **Gone** | reset to the initial interval | `−0.5`, down to `min_ease`; flag for re-study |
Defaults — overridable per learner with an optional `review_policy:` block in the learner profile:
```yaml
review_policy:
scheduler: fsrs-inspired
initial_interval: 1 # days after first learning / after a lapse
ease: 2.5 # starting multiplier
min_ease: 1.3
max_ease: 3.5
max_interval: null # null = unbounded; set e.g. 365 to cap growth
fuzz: true # ±5% jitter on computed due dates so notes don't pile up on one day
```
Notes are never "done": a long interval just means the topic comes up rarely. Legacy notes whose `review:` field is a plain list (`[+1d, +3d, +7d]`) still work — take the last entry as the current interval with default ease, and migrate them to the block format at this review.
## Running a review session
1. Read the review queue from the dashboard:
- **Markdown mode** (default): read `Learning/Dashboard.md` → **Up for review** section (notes whose next review date has passed).
- **Html mode** (`output_format.dashboard: html` in the learner profile): read the `agent-tutor-state` JSON island at the top of `Learning/Dashboard.html`'s `<body>` — its `due_notes[]` entries (note, due date, interval) are the queue. Map each entry back to its actual note file under `Learning/<Subject>/notes/` (island labels are display names); the thin `Dashboard.md` hub is a signpost — never parse it. No `Dashboard.html` yet → the fallback below applies.
- If the dashboard looks stale in either mode, scan `Learning/<Subject>/notes/*/` frontmatter directly — note `review:` frontmatter is always the scheduling authority.
2. **Recall first, always.** For each note: ask the user to explain the topic from memory *before* showing anything. ("Explain closures to me as if I'd never heard of them.")
3. Judge the recall against the note's key takeaways, then apply the schedule table:
- **Solid** → apply the solid row.
- **Shaky but recoverable** → show the key takeaways, have them re-explain; apply the shaky row only if the second attempt is clean, otherwise treat as gone.
- **Gone** → mark for re-study: re-open the lesson, re-teach the gaps, apply the gone row (reset to the initial interval).
4. **Interleave**: mix topics from different phases/subjects in one session — interleaving is the point, don't review one phase in isolation.
5. End with a **mixed mini-quiz** (5–8 questions spanning everything reviewed today).
- If an interactive quiz tool is available (e.g. pi's `quiz`), use it.
- Otherwise run a chat quiz: one question at a time, wait for the answer, explain why the right option is right, track the score, report pass/fail vs 70% at the end.
6. Update each note's `review:` frontmatter, the Dashboard review queue, and append a session log entry (type `review`) to `Learning/<Subject>/logs/YYYY-MM-DD.md` with per-topic retention verdicts.
## Honesty rules
- Judge recall against the note's key takeaways, not against "close enough".
- Tell the user their actual retention state — "you've forgotten X entirely" is useful information.
- If more than half of a subject's notes fail review, say it plainly and propose a re-teach plan rather than patching holes.
## Dashboard maintenance
Keep the **Up for review** section current: one bullet per due note, sorted by due date:
```
- [[notes/03-closures]] — +7d interval, due 2026-08-24
```
Remove bullets after the review is logged. This list is what the tutor reads at session start, so it must be true.
**Html mode** (`output_format.dashboard: html` in the learner profile): the queue lives in `Dashboard.html` — the **Up for review** section and the `due_notes[]` of the island read in step 1. Update it at the end of the session by regenerating the html dashboard whole — a fresh island, overview and subject focus pages together, plus the thin `Dashboard.md` hub's counters, exactly the update-the-Dashboard moment every session ends with; never hand-patch the html.
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