Borrowing it
Nothing to install: this file belongs to HYH926/smart-learn. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/HYH926/smart-learn/main/.claude/commands/smart-review.mdgit clone --depth 1 https://github.com/HYH926/smart-learnWrote 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/commands/hyh926/smart-learn/smart-review)<a href="https://agentmods.dev/commands/hyh926/smart-learn/smart-review"><img src="https://agentmods.dev/badge/commands/hyh926/smart-learn/smart-review.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00738 |
| Opus 5 | $0.00019 | $0.00369 |
| Sonnet 5 | $0.00008 | $0.00148 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
smart-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
启动快速复习模式。用户想复习的主题是：$ARGUMENTS
如果用户未指定主题
- 用 Glob 列出
knowledge_store/**/*.md和knowledge_store/**/*_思维导图.md - 提取所有已学主题名，以列表呈现给用户选择
- 用户选择后进入复习
复习流程（只读，不修改任何学习记录）
第1步：精华速览
读 knowledge_store/{主题}.md 或 knowledge_store/{主题slug}.md，提取核心公式+三个关键点+一句话类比，以卡片格式呈现：
📇 {主题} — 复习卡片
━━━━━━━━━━━━━━━━━━━━
🎯 {核心公式}
🔑 {关键点1} | {关键点2} | {关键点3}
🏗️ {一句话类比}
🏷️ {关键词}
如果笔记不存在，从完整学习报告的"步骤5"部分提取。
第2步：薄弱点回顾（间隔复习优先）
从笔记中提取 ⚠️ 薄弱点列表。解析每条薄弱点的 （首次标记：YYYY-MM-DD） 时间戳。
按"最久未复习"排序（距今天数 = 优先级）：
⚠️ 薄弱点回顾（按紧急度排序）
🕐 30天前 — 联合索引最左前缀（首次标记：2026-05-10）
🕐 15天前 — RESTful适用边界（首次标记：2026-05-25）
🕑 7天前 — CAP定理混淆（首次标记：2026-06-03）
逐条展示。对每条：
- 先让用户尝试用自己的话解释（主动回忆效应）
- 然后给出正确理解
- 确认用户理解后，更新该薄弱点的时间戳为今天（"复习过"）
- 不评分，纯回顾
第3步：可选自测
询问用户："要做一道薄弱点自测题吗？"
- 如果"是" → 从薄弱概念中选 1-2 个出题（单选题或简答），点评但不记录
- 如果"否" → 展示思维导图路径，结束
第4步：收尾
告知用户：
- 📝 完整笔记：
knowledge_store/{主题}.md - 🧠 思维导图：
knowledge_store/{主题}_思维导图.md（可在 VSCode 中打开渲染） - ⏱️ 复习时间：记录当前时间
约束
- 这是纯复习模式，不走五步学习流程
- 如果找不到该主题的笔记，告知用户并列出所有可用主题
- 如果知识库为空，告知用户"还没有学习记录，用 /smart-learn 开始学习吧"
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 First seen · 62 lines · 39 tokens per session scan A c964a052f391
smart-review is a command published in the GitHub repository HYH926/smart-learn (21 stars, last pushed 2mo ago), licensed MIT. It adds 39 tokens to every session and 738 once invoked, about $0.0002 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 2026-08-30.
Other commands, from other repositories
learn
Initialize a new learning topic $topic or continue learning an existing one using the FASTER framework.
progress
Show detailed progress report for current learning topic.
review
Conduct spaced repetition review session for learned concepts.
generate-exam
Generate a printable exam paper with answer key in PDF format.
daily-okr
Run a daily knowledge compound loop (7 KR). Invoke with /daily-okr or "start my daily review".
learn
Start learning a new topic — asks clarifying questions, researches resources, and creates a structured learning plan.