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 negative-case-findergit 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/negative-case-finder)<a href="https://agentmods.dev/skills/yipng05-max/-skills/negative-case-finder"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/negative-case-finder/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/negative-case-finder"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/negative-case-finder.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 | $0.00159 | $0.03124 |
| Opus 5 | $0.00079 | $0.01562 |
| Sonnet 5 | $0.00032 | $0.00625 |
| Haiku 4.5 | $0.00016 | $0.00312 |
Grade A, and why
negative-case-finder 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
负面案例分析工具（Negative Case Analysis）
本 skill 基于 Lincoln & Guba（1985）的负面案例分析方法及 Charmaz（2014）的理论抽样逻辑， 协助研究者系统识别与当前命题不一致的案例，并通过类型区分来推进命题修订，而非简单否定命题。
核心立场：负面案例不是命题的敌人，而是命题的精化器。 一个不能被负面案例修订的命题，要么是已经精确的好命题，要么是没有边界的泛化陈述。
启动：获取必要信息
触发后，收集以下信息：
必填 1：当前暂定命题
一句话格式最佳，例如：
"在有正式雇佣经历对照的平台工人中，时间自由叙事的内部裂缝以'不接单罪恶感'为表征， 揭示平台将工作时间边界的管理责任从组织转移至工人个体。"
必填 2：支撑命题的关键材料
可以是：
- 编码列表（指出哪些编码支撑了这个命题）
- 原始访谈片段（支撑命题的核心段落）
- 已有分析备忘录（直接粘贴）
选填 3：研究者已发现的不一致之处（若有）
如果研究者已注意到某些让命题不稳定的材料，先行标注，skill 优先处理。
选填 4：当前研究进展阶段
- 早期编码阶段 → 重点做"数据不足"类识别，推动理论抽样
- 中期分析阶段 → 重点做"边界案例"类识别，精化命题范围
- 后期理论建构阶段 → 重点做"真反例"类识别，检验命题稳健性
执行流程
收到信息后，自动连续执行以下三个阶段，无需每步等待用户确认。
第一阶段：命题结构解析
在寻找负面案例之前，先解析命题的内部逻辑结构，输出：
- 命题的条件范围：命题声称在什么条件下成立？（显性条件 + 隐性预设）
- 命题的核心机制：命题声称发生了什么过程？（A → B 的逻辑链条）
- 命题的可证伪点：什么样的发现会对命题构成威胁？
- 威胁类型一：条件出现但结果不出现
- 威胁类型二：结果出现但条件不出现
- 威胁类型三：结果出现但机制不同
这一步的目的是让后续的负面案例识别有明确的靶向，而非泛泛寻找"不同意见"。
第二阶段：负面案例系统识别
根据命题结构，从提供的材料中（或基于对材料的推断）识别不一致之处， 将每个不一致归入以下四种类型之一，并给出对应处理建议：
类型一：真反例（Genuine Disconfirming Case）
定义：满足命题的全部前提条件，但结果与命题预测相反，且无法通过调整边界条件来解释。
识别信号：
- 受访者具备命题声称的全部条件
- 其行为/感受/叙述与命题预期明显相悖
- 研究者无法用"这是个特殊情境"来合理化这个偏差
分析要求：
- 描述这个案例的具体情况
- 解释它为什么是真反例而非边界案例
- 提出命题修订方向：是否需要修改核心机制，而非只调整边界条件？
输出格式：
[真反例] 案例描述：
机制威胁：该案例挑战命题中的哪个环节？
修订方向：命题可能需要如何根本性修订？
类型二：边界案例（Boundary/Limit Case）
定义：命题在某个条件范围内成立，但在另一个条件范围内不成立。这类案例不否定命题， 而是揭示命题的适用边界，帮助研究者精化命题的条件范围。
识别信号：
- 案例与命题主体案例的关键差异在于某个情境条件
- 改变这个条件后，结果发生系统性变化
- 研究者可以用"当X时，命题成立；当Y时，命题不成立"来描述这种差异
分析要求：
- 描述这个案例与支撑命题的主体案例有何不同
- 识别"关键转折条件"——是什么让这个案例落在命题边界之外？
- 提出命题精化方向：在命题中加入什么条件限定可以更准确？
输出格式：
[边界案例] 案例描述：
关键转折条件：是什么条件差异导致了不同结果？
命题精化方向：可以在命题中加入"当……时"来更准确地界定适用范围
精化后命题草稿：
类型三：维度差异（Dimensional Variation）
定义：案例表面上与命题不一致，实质上是同一现象在不同程度、阶段或侧面的表现。 这类案例不否定命题，而是提示命题所描述的现象是有内部变异的，可能需要引入维度分析。
识别信号：
- 案例中的现象"好像是同一件事，但表现形式不同"
- 可以用强度、频率、时间阶段、关系类型等维度来区分
- 核心机制相同，但结果的表现形式或程度有差异
分析要求：
- 描述这个案例与主体案例的相似之处（确认核心机制相同）
- 识别维度差异的来源（是什么导致了表现形式的差异？）
- 提出分析深化方向：是否需要在命题中引入维度概念来容纳这种变异？
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 · 273 lines · 159 tokens per session scan A 3dcee42adcbd
negative-case-finder is a skill published in the GitHub repository yipng05-max/-skills (285 stars, last pushed 4mo ago), licensed MIT. It adds 159 tokens to every session and 3,124 once invoked, about $0.0008 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…