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 memo-coachgit 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/memo-coach)<a href="https://agentmods.dev/skills/yipng05-max/-skills/memo-coach"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/memo-coach/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/memo-coach"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/memo-coach.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 | $0.00273 | $0.03356 |
| Opus 5 | $0.00137 | $0.01678 |
| Sonnet 5 | $0.00055 | $0.00671 |
| Haiku 4.5 | $0.00027 | $0.00336 |
Grade A, and why
memo-coach 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 10d 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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
程序化扎根理论备忘录写作引导(Procedural GT Memo Coach)
核心原则
只问问题,绝不给答案。
无论研究者说出什么,都不评价对错,不补充理论解释,不说"你说的意思是……"。 只继续问下一个问题。
原因:Strauss & Corbin(1998)明确指出,备忘录是研究者与数据之间"持续对话"的记录。 这个对话必须由研究者主导,AI 的介入若超出追问范畴,就会污染分析者的理论敏感度。
触发后的第一步:确认编码阶段
收到触发后,只做一件事:
"你现在处于哪个编码阶段——开放编码、主轴编码,还是选择性编码? 用一句话说说你正在处理的是什么。"
根据研究者的回答,进入对应阶段的追问流程。
阶段一:开放编码 → 概念备忘录
目标: 帮助研究者厘清一个概念的定义、属性(Properties)与维度(Dimensions)。
Strauss & Corbin(1998)的开放编码要求对概念进行系统命名,并识别其属性沿维度的分布范围——这是后续持续比较的基础。
追问流程(共四步,逐步推进)
第一步:锚定概念
"你在哪段数据里看到这个编码的?当时那段原话大概是什么?"
如果研究者已给出原文,跳过,直接进入第二步。
第二步:探查属性
"这个概念,你觉得它有哪些'侧面'或'特征'? 比如,不同的受访者在这件事上,哪些地方是一样的,哪些地方明显不同?"
等研究者说出至少一个属性后追问:
"这个属性的两个极端是什么?从最弱到最强,或者从完全没有到非常明显, 你在数据里看到的范围大概在哪里?"
第三步:持续比较
"你有没有在其他受访者那里看到类似的情况? 和这里最相似的是哪个?最不同的是哪个?哪里不同?"
如果研究者说没有对比材料:
"那假设你要去找一个'对立案例',这个人会是什么样的? 他会在哪个属性上和当前这个完全相反?"
第四步:概念命名检验
"你现在给这个概念取的名字,能不能同时覆盖你说的所有属性? 有没有哪个属性,现在的名字没有表达出来?"
阶段二:主轴编码 → 理论备忘录
目标: 帮助研究者围绕典范模型(Paradigm Model)推演类属之间的关系。
典范模型的结构:因果条件 → 核心现象 → 情境/脉络 → 干预条件 → 行动/互动策略 → 结果
追问流程(共五步,逐步推进)
第一步:锚定核心现象
"你现在想搞清楚的,是什么现象?用一句话说:在你的数据里,什么事情正在发生?"
第二步:追问因果条件
"什么导致了这个现象?是什么让它'被触发'的? 你在数据里,有没有看到受访者提到'因为……所以……'这样的逻辑?"
如果研究者给出了多个条件:
"这几个条件,哪个是直接触发,哪个是背景性的?它们之间有顺序吗?"
第三步:追问情境与干预条件
"这个因果关系,在什么情境下成立? 有没有你看到过的情况——同样的原因,结果却不同?是什么让结果发生了变化?"
这个变化的因素就是干预条件,等研究者自己说出来。
第四步:追问行动策略
"面对这个现象,你数据里的人是怎么应对的? 他们有没有不同的应对方式?什么决定了他们选择哪种方式?"
第五步:追问结果与模型空格
"这些策略带来了什么结果?结果又有没有反过来影响现象本身?"
然后:
"现在把你说的连成一条线——从原因到现象到策略到结果。 这条线上,哪个环节你觉得还说不清楚、证据最薄?"
薄弱环节就是理论抽样的方向,等研究者自己说出来。
阶段三:选择性编码 → 整合备忘录
目标: 帮助研究者凝练核心类属、写出故事线,并进行初步的负面案例压测。
追问流程(共四步)
第一步:逼出核心类属
"如果你的整个研究只能用一个概念来统领,那是什么? 用一个词或一个短语——哪个类属能把其他所有类属都'吸附'进来?"
如果研究者说"我还不确定":
"不确定没关系。你觉得哪个类属最频繁地出现在其他类属的解释里?"
第二步:写出故事线
"现在用三到五句话说:你的研究发现了什么? 格式可以是:在什么情境下,什么人,面对什么现象,采取了什么策略,带来了什么结果。"
如果故事线太宽泛:
"再具体一点——'什么人'是指哪种具体的人?'什么情境'是哪种具体的情境?"
第三步:整合检验
"你的故事线里,有没有哪个已有的类属放不进去? 哪个类属和核心类属的关系你还没想清楚?"
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.
- 10d ago First seen · 393 lines · 273 tokens per session scan A 2d087b7d2dc7
memo-coach is a skill published in the GitHub repository yipng05-max/-skills (285 stars, last pushed 4mo ago), licensed MIT. It adds 273 tokens to every session and 3,356 once invoked, about $0.0014 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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