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 skills/owenliang60-ship-it/knowledge-mgmt/notenpx skills add owenliang60-ship-it/knowledge-mgmt --skill notegit clone --depth 1 https://github.com/owenliang60-ship-it/knowledge-mgmtWrote 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/owenliang60-ship-it/knowledge-mgmt/note)<a href="https://agentmods.dev/skills/owenliang60-ship-it/knowledge-mgmt/note"><img src="https://agentmods.dev/badge/skills/owenliang60-ship-it/knowledge-mgmt/note.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 | $0.00142 | $0.04349 |
| Opus 5 | $0.00071 | $0.02174 |
| Sonnet 5 | $0.00028 | $0.00870 |
| Haiku 4.5 | $0.00014 | $0.00435 |
Grade A, and why
note 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 6d 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/note Command
将对话中的研究内容整理为 研究摘要 + 原子化笔记卡片，存入 Obsidian。
核心理念：地图 + 石头
每次 /note 产出两层内容：
- 研究摘要（地图） — 一张完整的、叙事性的卡片，保留讨论的脉络、推理链条和结论。读这张卡片就能还原整个研究过程。
- 原子卡片（石头） — 从摘要中提炼的独立知识点，每张可脱离上下文复用。摘要中会引用这些卡片的关键词。
地图让石头有意义，石头让地图可链接。
定位
/note 是 vault 的唯一写入通道（SCHEMA.md §2）。上游的 ingest 对话由 /read / /insights 完成，下游的 capture 与本地记忆由 /log / /journal 承接。本表横向对比这五个角色，方便理解"哪类事情该走哪个 skill"：
| Skill | 职责 | 输出 |
|---|---|---|
/read |
深度阅读 — 学术论文结构化分析 | 对话中的分析报告 → 喂给 /note |
/insights |
商业洞察 — 文章模式提取 | 对话中的洞察报告 → 喂给 /note |
/note |
知识沉淀 — 研究摘要 + 原子卡片（dual-proposal） | Obsidian 卡片组（唯一写入通道） |
/log |
记忆捕获 — 决策、偏好、洞察 | L2 long-term-memory.md |
/journal |
进度记录 — 做了什么 | 本地 journal + Obsidian journal |
双链标记规范
- 【专有名词】：学术概念、理论名称、人名、模型名、基因/分子/药物
- 例：【奖励预测误差】、【Kent Berridge】、【COMT Val158Met】、【SSRI】
- 加粗：核心结论或关键洞察
- 例：多巴胺编码的是"值不值得出力"，而非快乐本身
- 两者可叠加：【消退学习】是记忆改写的神经科学基础
标记选择速查
| 内容类型 | 标记 | 示例 |
|---|---|---|
| 学术概念/理论 | 【】 | 【奖励预测误差】、【心理对比】 |
| 人名 | 【】 | 【Wolfram Schultz】、【Berridge】 |
| 基因/分子/药物 | 【】 | 【DRD4】、【COMT】、【SSRI】 |
| 核心结论 | 加粗 | 受体密度67%由遗传决定 |
| 关键区分 | 加粗 | wanting ≠ liking |
| 重要概念+结论 | 叠加 | 【快感缺失】是抑郁症的核心特征 |
Behavior
Step 1: 扫描对话，理解研究脉络
回溯对话（或指定范围），梳理：
- 研究的起因和主题是什么
- 讨论经历了哪些阶段/转折
- 得出了哪些关键结论
- 哪些知识点值得独立成卡
Step 2: 撰写研究摘要（地图卡片）并展示给用户
研究摘要是一张 完整的、翔实的 卡片。此阶段只写摘要，不创建原子卡片。 所有专有名词用【】标记作为候选概念。
摘要结构：
# [研究主题] — 研究摘要
> 日期：YYYY-MM-DD | 来源：[对话起因，如"某篇文章分析"、"某个问题探究"]
## 研究背景
[1-2 段：为什么研究这个话题，起因是什么]
## 核心发现
### [子主题1]
[翔实内容：保留关键论据、数据、推理链条。不是缩写，而是精炼——删掉冗余但保留实质。]
### [子主题2]
[同上]
### [子主题N]
[同上]
## 关键结论
[3-5 条最重要的 takeaway，用编号列表]
## 延伸问题
[对话中浮现但未深入的问题，供后续探索]
注意：此阶段不写"相关概念"section，等用户选完概念后再生成。
摘要写作原则：
- 翔实优先：宁可长一点也不要丢失关键信息。读者应该只看摘要就能获得 80% 的对话价值
- 保留推理链：不只是结论，还要保留"为什么得出这个结论"的论证过程
- 保留数据：具体数字、实验设计、百分比等不要省略
- 保留争议：如果对话中有质疑或修正某个观点，这个过程本身也要记录
- 自然嵌入【】标记：在行文中自然使用【】标记专有名词。此阶段【】是候选标记，不是最终双链
What ships with it
1 file 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.
- 6d ago First seen · 368 lines · 142 tokens per session scan A 6843d4d69f1f
note is a skill published in the GitHub repository owenliang60-ship-it/knowledge-mgmt (37 stars, last pushed 4mo ago), licensed MIT. It adds 142 tokens to every session and 4,349 once invoked, about $0.0007 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.
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