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 yugash007/edu-agent-skills --skill spaced-repetitiongit clone --depth 1 https://github.com/yugash007/edu-agent-skillsWrote 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/yugash007/edu-agent-skills/spaced-repetition)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/spaced-repetition"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/spaced-repetition/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/yugash007/edu-agent-skills/spaced-repetition"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/spaced-repetition.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 | $0.00024 | $0.00821 |
| Opus 5 | $0.00012 | $0.00411 |
| Sonnet 5 | $0.00005 | $0.00164 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
spaced-repetition 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 12d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Schedule review of previously-learned concepts at expanding intervals to exploit the spacing effect. Manages due dates, adjusts intervals based on recall quality, and surfaces overdue items before they decay.
Activation
- Review session is due based on schedule. Learner initiates review.
flashcardsdeck has due items. Sustained learning track (3+ sessions) needing interval maintenance. - Skip if: one-shot session with no continuity. Concept not yet learned. Learner declines scheduling.
- Routing: overdue items take priority at session start. Coordinate with
flashcardsfor card-level scheduling. Feed interval data tolearning-memory.
Inputs
- Flashcard/item schedule with due dates, learner's current session, items from
learning-memory.
Interval Algorithm (Simplified SM2)
Each item has an interval (days) and ease factor (EF, 1.3–2.5). Initial: 1 day → 3 days → then formula.
| Score | Label | Interval Rule | EF Change |
|---|---|---|---|
| 0 | Failed | Reset to 1 day | EF -= 0.2 (min 1.3) |
| 1 | Hard | Stay at current | EF -= 0.1 |
| 2 | Good | Interval × EF | No change |
| 3 | Easy | Interval × EF × 1.3 | EF += 0.1 (max 2.5) |
Mastery threshold: EF > 2.4, interval > 60 days, 5 consecutive successes → archive.
Workflow
- Detect Due Items — Check
next_review ≤ today. Sort by overdue duration (most overdue first). Report count. - Scope Session — ≤10 due: review all. >10: prioritize by overdue + weak-area overlap, cap at 15. Report deferrals.
- Execute Review — Present front, wait for learner response, reveal back. Self-score: Failed/Hard/Good/Easy. Compute new interval immediately. Never reveal answer before attempt.
- Handle Failures — Score 0: reset to 1 day, re-test at end of current session. Failed 3 sessions in a row: flag for
misconception-detector. - Update Schedule — Output updated schedule. Show items due in next 7 days. Warn about upcoming review spikes.
- Onboard New Items — Fresh concept → add at interval=1 day. Confirm concept is understood first (not still unclear).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 68 lines · 24 tokens per session scan A 4628c27eb96d
spaced-repetition is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 821 once invoked, about $0.0001 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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