memo-coach

memo-coach is a skill for Claude Code, Codex from yipng05-max/-skills. It costs 273 tokens per session (3,356 once invoked), scanned A, original, MIT.

A Chinese-language question guide for writing research memos during grounded theory analysis, a method for building concepts from collected data. It asks questions suited to open, axial, or selective coding stages without supplying the analysis itself.

In plain words
What is it for?
Use it to examine a code, compare cases, explore relationships between categories, or integrate a grounded theory storyline through guided questions.
Why use it?
It helps researchers make their own concepts and relationships clearer while keeping interpretation with the researcher. The questions focus on evidence, properties, comparisons, causes, actions, and results.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to examine a code, compare cases, explore relationships between categories, or integrate a grounded theory storyline through guided questions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yipng05-max/-skills/memo-coach
Install

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.

Any agent
npx skills add yipng05-max/-skills --skill memo-coach
Clone the repo
git clone --depth 1 https://github.com/yipng05-max/-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for memo-coach

README.md
[![agentmods](https://agentmods.dev/badge/skills/yipng05-max/-skills/memo-coach/github.svg)](https://agentmods.dev/skills/yipng05-max/-skills/memo-coach)
Your own site
<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.

agentmods 80×15 button for memo-coach

Your own site · 80×15
<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>
Per session 273 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,356 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash 2d087b7d2dc7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

memo-coach/SKILL.md · 393 lines

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?"

Read the full file on GitHub · 393 lines

Changes

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.

  1. 10d ago First seen · 393 lines · 273 tokens per session scan A 2d087b7d2dc7

Subscribe to this mod's changes

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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