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 ta-discussion-writergit 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/ta-discussion-writer)<a href="https://agentmods.dev/skills/yipng05-max/-skills/ta-discussion-writer"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/ta-discussion-writer/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/ta-discussion-writer"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/ta-discussion-writer.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 | $0.00093 | $0.01919 |
| Opus 5 | $0.00046 | $0.00959 |
| Sonnet 5 | $0.00019 | $0.00384 |
| Haiku 4.5 | $0.00009 | $0.00192 |
Grade A, and why
ta-discussion-writer 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 11d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TA 讨论与结论章节写作
将主题分析的发现转化为与理论和文献的对话，形成讨论与结论章节。 讨论章节是论文中理论贡献最集中的地方——它回答的不是"发现了什么"，而是"这意味着什么"。
启动：收集必要信息
启动时收集（在 ta-research-workflow 中已有的信息自动复用）：
- 研究发现：主题汇总表 + 发现叙事（来自
ta-findings-writer输出，或研究者提供） - 理论框架：选定的理论、核心概念（来自
ta-framework-builder输出，或研究者提供） - 文献综述要点：核心争论、既有研究的主要结论（来自
literature-review-writer输出，或研究者提供） - 研究问题
- 理论定位（A/B/C）——若在工作流中已确认，自动沿用
第一步：确认理论定位对讨论章节的工作分配
根据理论定位，讨论章节承担不同的理论工作：
A（理论驱动）：讨论的工作是深化和检验已有框架
- 哪些发现验证了理论命题？用具体主题和引语说明
- 哪些发现修正了理论命题？需要说明在什么条件下理论失灵
- 哪些发现挑战或扩展了理论？这是最有价值的贡献所在
B（经验驱动）：讨论承担全部理论建构工作
- 先从发现中归纳机制：这些主题合在一起，说明了什么社会过程？
- 再召唤理论：哪个理论概念最能捕捉这个机制？两者在哪里重合、哪里分歧？
- 最终定位贡献：本研究的涌现理论对哪个学术对话做出了什么推进？
C（敏感性概念）：讨论在发现与理论之间来回往复，理论对话最为丰富
- 发现如何印证了理论透镜所"照亮"的维度？
- 发现中有哪些理论透镜没有照到的地方？这些"盲区"说明了什么？
- 如何在与理论的对话中深化、修正、或推进理论本身？
第二步：生成讨论章节
部分 1：跨主题综合（必须有）
将各主题整合为一个整体性理解，回答研究问题。
写作要求：
- 不是对每个主题的重复总结（"第一个主题说了X，第二个主题说了Y……"）
- 而是提炼多个主题合在一起揭示的核心机制、过程或张力
- 一段话回答：这些发现合在一起，告诉我们关于[研究现象]的什么？
- 篇幅：1–2段，精炼有力
部分 2：理论对话（核心，必须有）
将发现与既有理论/文献展开实质性对话。
对话框架（逐一处理）：
2a. 一致性：发现与既有理论/文献在哪些地方吻合？
- 指出具体的理论命题或文献结论
- 说明哪个主题（哪条引语）支持这种吻合
- 不要只说"与XX研究一致"，要说明吻合的具体内容
2b. 差异性：发现与既有理论/文献在哪些地方不同？
- 这是最有价值的对话——说明现有理论在什么情境或条件下失灵
- 解释为什么会出现差异（情境差异、群体差异、时间差异……）
- 差异不是"矛盾"，而是理论修正或扩展的机会
2c. 推进性：发现对既有知识体系做了什么推进？
- 不要泛化（"丰富了……""拓展了边界……"）
- 具体说明：本研究通过[具体发现]，[修正/扩展/挑战/提出]了[具体概念或命题]
- 这是贡献声明的基础
对话质量红线：
- 所有涉及的文献引用必须真实存在；不确定时标注"此引用需研究者自行核实"
- 理论对话必须指向具体概念，不得只说"与XX理论一致"
- 如果 B 定位，理论是在此处首次引入的——需要简短交代理论的核心命题，然后立即展开对话
部分 3：理论贡献（必须有）
在理论对话的基础上，明确陈述本研究的理论贡献。
贡献必须通过三重检验：
- 理论对话检验：这个贡献进入了哪场学术对话？对话的另一方是谁？
- "So What"检验：如果这个结论成立，对学科知识体系意味着什么？有什么后续含义？
- 新颖性检验：为什么没有其他研究已经做到了这一点？（情境新颖/方法新颖/理论视角新颖）
篇幅：1段，清晰、有力、不罗列
部分 4：研究局限（必须有，诚实标注）
三类局限，各处理一条：
- 方法局限：主题分析的局限（如研究者的理论敏感性影响编码；主题的建构性）
- 样本局限：研究对象的代表性（如情境特殊性、理论抽样的局限）
- 分析局限：本研究无法回答的问题（如长期过程、因果机制的验证）
写作要求：
- 局限不是"道歉"，而是对研究边界的诚实标注
- 每条局限附带"这意味着……研究者在解读结论时需要注意……"
- 不要罗列过多局限（3条足够），也不要局限过于宽泛（"样本量小"这类无实质内容的说法）
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.
- 11d ago First seen · 161 lines · 93 tokens per session scan A 7f3b80b295e4
ta-discussion-writer is a skill published in the GitHub repository yipng05-max/-skills (285 stars, last pushed 4mo ago), licensed MIT. It adds 93 tokens to every session and 1,919 once invoked, about $0.0005 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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