Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Mohamed-El-Sharqawy/agent-tutor --skill agent-tutor-reviewgit clone --depth 1 https://github.com/Mohamed-El-Sharqawy/agent-tutorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mohamed-el-sharqawy/agent-tutor/agent-tutor-review)<a href="https://agentmods.dev/skills/mohamed-el-sharqawy/agent-tutor/agent-tutor-review"><img src="https://agentmods.dev/badge/skills/mohamed-el-sharqawy/agent-tutor/agent-tutor-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mohamed-el-sharqawy/agent-tutor/agent-tutor-review"><img src="https://agentmods.dev/badge/skills/mohamed-el-sharqawy/agent-tutor/agent-tutor-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.01320 |
| Opus 5 | $0.00042 | $0.00660 |
| Sonnet 5 | $0.00017 | $0.00264 |
| Haiku 4.5 | $0.00008 | $0.00132 |
Grade A, and why
agent-tutor-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
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:
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
- 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: htmlin the learner profile): read theagent-tutor-stateJSON island at the top ofLearning/Dashboard.html's<body>— itsdue_notes[]entries (note, due date, interval) are the queue. Map each entry back to its actual note file underLearning/<Subject>/notes/(island labels are display names); the thinDashboard.mdhub is a signpost — never parse it. NoDashboard.htmlyet → the fallback below applies. - If the dashboard looks stale in either mode, scan
Learning/<Subject>/notes/*/frontmatter directly — notereview:frontmatter is always the scheduling authority.
- Markdown mode (default): read
- 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.")
- 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).
- Interleave: mix topics from different phases/subjects in one session — interleaving is the point, don't review one phase in isolation.
- 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.
- If an interactive quiz tool is available (e.g. pi's
- Update each note's
review:frontmatter, the Dashboard review queue, and append a session log entry (typereview) toLearning/<Subject>/logs/YYYY-MM-DD.mdwith per-topic retention verdicts.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago Changed · +5 lines ab55fe325043
- 12d ago First seen · 77 lines · 84 tokens per session scan A 044a6a703662
agent-tutor-review is a skill published in the GitHub repository Mohamed-El-Sharqawy/agent-tutor (3 stars, last pushed 12d ago), licensed MIT. It adds 84 tokens to every session and 1,320 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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