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-searchgit 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-search)<a href="https://agentmods.dev/commands/hyh926/smart-learn/smart-search"><img src="https://agentmods.dev/badge/commands/hyh926/smart-learn/smart-search.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 | $0.00028 | $0.00457 |
| Opus 5 | $0.00014 | $0.00229 |
| Sonnet 5 | $0.00006 | $0.00091 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
smart-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
搜索你的个人知识库。
用户搜索的关键词是：$ARGUMENTS
搜索流程
1. 全量检索
用 Grep 在 knowledge_store/ 下所有 .md 文件中搜索关键词（忽略思维导图和 checkpoint 文件）：
# 排除思维导图和状态文件，只搜学习笔记
grep -rli "$ARGUMENTS" knowledge_store/*.md --ignore-case 2>/dev/null | grep -v "思维导图" | grep -v "checkpoint" | grep -v "mindmap_state"
2. 展示结果
对每个匹配文件，提取包含关键词的上下文段落（前后各 1 行），按主题分组：
🔍 搜索「{关键词}」 — 找到 N 处匹配
📁 {主题A} → knowledge_store/{文件A}.md
┌ 上下文...
│ ...{关键词}...
└ ...
📁 {主题B} → knowledge_store/{文件B}.md
┌ 上下文...
│ ...{关键词}...
└ ...
🧠 思维导图中也找到匹配：
- knowledge_store/{主题A}_思维导图.md
3. 附加操作
搜索完成后询问：
- "要打开某篇完整笔记复习吗？" → 用户选主题 → 切换到
/smart-review 主题名流程 - 如果无结果 → "知识库中未找到「{关键词}」。用 /smart-learn 学习这个主题？"
约束
- 纯只读，不写任何文件
- 如果 knowledge_store 为空 → "知识库为空，用 /smart-learn 开始学习吧"
- 搜索范围：仅
knowledge_store/目录下的.md文件
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.
- 5d ago First seen · 49 lines · 28 tokens per session scan A 08b017fbe552
smart-search is a command published in the GitHub repository HYH926/smart-learn (21 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 457 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 2026-08-30.
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".