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 agentmods add commands/hyh926/smart-learn/smart-statsgit 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-stats)<a href="https://agentmods.dev/commands/hyh926/smart-learn/smart-stats"><img src="https://agentmods.dev/badge/commands/hyh926/smart-learn/smart-stats.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 | $0.00032 | $0.00697 |
| Opus 5 | $0.00016 | $0.00349 |
| Sonnet 5 | $0.00006 | $0.00139 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
smart-stats 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 4d 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
显示 Smart Learn 学习统计数据。
统计维度
1. 学习概览
用 Glob 扫描 knowledge_store/ 目录：
# 统计 .md 笔记数量（排除思维导图和checkpoint）
ls knowledge_store/*.md 2>/dev/null | grep -v "思维导图" | grep -v "checkpoint" | wc -l
输出：
- 📚 总学习主题数
- 🧠 总概念数（从各笔记的概念地图中提取）
- ⚠️ 总薄弱点数（从各笔记中提取 ⚠️ 标记）
2. 主题列表
列出每个已学主题的关键信息：
| 主题 | 学习日期 | 概念数 | 薄弱点 | 关键词 |
|---|---|---|---|---|
| {主题1} | {日期} | {N} | {N} | {kw} |
| ... | ... | ... | ... | ... |
3. 薄弱点总览（含间隔复习状态）
聚合所有主题的薄弱点，解析 （首次标记：YYYY-MM-DD） 时间戳，计算距今天数：
⚠️ 薄弱点分布
🕐 距今天数 主题 薄弱点
────────────────────────────────────────
30天 RAG基础知识 向量嵌入选型陷阱
15天 系统架构设计 联合索引最左前缀
7天 全栈开发 RESTful适用边界
⚠ 最近30天未复习的薄弱点：N 个
✅ 最近7天已复习的薄弱点：M 个
提示：
- "🕐 超过 14 天未复习的薄弱点建议用 /smart-review {主题名} 回顾"
- "📈 薄弱点复习率：{已复习数}/{总数}"
4. 知识地图总览
如果存在多个主题，输出跨主题关联：
🗺️ 知识网络
{主题A} ──关联── {主题B}
{主题A} ──关联── {主题C}
...
5. 知识库路径
- 📝 Markdown 笔记：
knowledge_store/ - 🧠 思维导图：
knowledge_store/*_思维导图.md - 📄 Word 文档：
knowledge_store/*_学习笔记.docx（如有）
约束
- 纯只读，不写任何文件
- 如果 knowledge_store 为空，告知"还没有学习记录，用 /smart-learn 开始学习吧"
- 所有数据从已有 Markdown 笔记和 checkpoint 文件中提取
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.
- 4d ago First seen · 69 lines · 32 tokens per session scan A 937c82752f7c
smart-stats is a command published in the GitHub repository HYH926/smart-learn (21 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 697 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
OPSX: Onboard
Guided onboarding - walk through a complete OpenSpec workflow cycle with narration.
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".