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 yipng05-max/-skills --skill analytic-memogit clone --depth 1 https://github.com/yipng05-max/-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/yipng05-max/-skills/analytic-memo)<a href="https://agentmods.dev/skills/yipng05-max/-skills/analytic-memo"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/analytic-memo/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/yipng05-max/-skills/analytic-memo"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/analytic-memo.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 | $0.00216 | $0.01638 |
| Opus 5 | $0.00108 | $0.00819 |
| Sonnet 5 | $0.00043 | $0.00328 |
| Haiku 4.5 | $0.00022 | $0.00164 |
Grade A, and why
analytic-memo 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 9d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
分析备忘录(Analytical Memo)
分析备忘录是质性研究中捕捉分析动能的核心工具。Charmaz(2014)将备忘录定义为 研究者与数据之间持续进行的智识对话,而非填写分类表格的形式操作。 此 skill 的设计原则:研究者只管说出想法,工具负责追问和结构化。
启动:获取必要信息
触发后,只需收集两项信息(其余由 skill 自动判断):
-
触发内容:用户输入的编码片段、类属名称、原始资料段落、初步想法或困惑 (直接使用用户的原始表述,不要要求用户重新整理或分类)
-
保存路径(可选):若未提供,默认保存到
~/Documents/research-memos/
若用户在之前对话中已提供研究背景(研究主题、研究问题),直接沿用,不重复询问。
内部识别逻辑(对用户不可见)
根据用户输入,自动判断分析方向,不向用户暴露这个判断过程:
→ 概念深化(输入是单个编码或类属,附带描述或疑问) 追问:这个概念的核心含义和边界是什么?在哪些条件下更显著或消退? 与已有理论概念有何联系或张力?它暗示了什么理论主张?
→ 关系假设(输入涉及两个或以上概念,且包含关系词:关系、影响、导致、联系、之间) 追问:这个关系的性质是什么(因果、条件、并行、对立)? 数据中有哪些直接证据?在什么情境下成立或不成立(边界条件)?
→ 负面案例(输入包含反差信号:但是、例外、不符合、反而、奇怪、矛盾、和别人不一样) 追问:这是真正的反例,还是揭示了边界条件? 是否需要修订现有类属或理论假设?修订方向是什么?
→ 反身性(输入包含研究者自我指涉:我觉得、我担心、我是否、我的立场、我注意到自己) 追问:研究者的哪种预设或情绪可能影响了这段分析? 这个反思对理论抽样或研究设计有什么启示?
→ 综合展开(输入混合多种信号,或信号不明确) 先用一句话锚定这段想法的核心,再沿最主要的分析方向展开。
发展阶段判断(参考 Birks, Chapman & Francis, 2008)
根据用户描述的研究进展,在文件 frontmatter 中自动标注阶段:
preliminary:研究者处于开放编码早期,想法贴近数据、印象式interim:开始跨类属思考,建立概念间联系advanced:涉及核心类属、理论命题或整体理论框架
判断依据:
- "刚开始编码"/"第一份访谈" → preliminary
- 提到多个类属的关系/"开始看到模式" → interim
- 提到核心类属/"理论框架"/"饱和" → advanced
- 无法判断 → 留空,不强行填写
备忘录生成
按以下结构生成分析内容(对话中展示,同时写入文件):
文件 frontmatter
---
date: YYYY-MM-DD HH:MM
stage: <preliminary | interim | advanced>
tags: [<研究主题关键词>, <核心概念名>]
status: draft
---
正文结构
## 触发材料
用户原始输入,完整引用,一字不改。
## 核心追问
根据内部识别的分析框架展开,400–600字。 语气是研究者自己在思考,不是"建议你思考"——用第一人称或直接陈述。 追问应该推进分析,而不是复述输入内容。
## 暂定命题
一句话。陈述式,可被证伪。格式:"[条件/情境]下,[现象/机制],因为[初步解释]。" 这是备忘录的核心产出,逼迫研究者从模糊感觉走向可检验的命题。
## 待检验
- 具体的、可操作的检验行动(回到哪份数据?找什么?问什么?)
- 如需理论抽样,明确说明"去找什么类型的受访者/情境"
保存文件
文件名格式:YYYY-MM-DD_<核心概念词(2-4字)>.md
保存步骤:
- 使用 Bash 工具确认目录存在,若不存在则创建:
mkdir -p <路径> - 使用 Write 工具写入文件
保存完成后,在对话中只输出三项:
- 文件完整路径
- 暂定命题(重复一次,让研究者看清楚这句话)
- 待检验清单
不要总结备忘录全文,不要复述追问过程。
连续记录模式
一次对话中多次触发时,每条生成独立文件,自动累积。 对话结束时若生成了 2 条以上,汇总告知:本次共生成 N 条备忘录,列出路径和各自的暂定命题。
参考文献(供课程引用)
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.
- 9d ago First seen · 150 lines · 216 tokens per session scan A e9434e6093aa
analytic-memo is a skill published in the GitHub repository yipng05-max/-skills (285 stars, last pushed 4mo ago), licensed MIT. It adds 216 tokens to every session and 1,638 once invoked, about $0.0011 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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