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-methods-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-methods-writer)<a href="https://agentmods.dev/skills/yipng05-max/-skills/ta-methods-writer"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/ta-methods-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-methods-writer"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/ta-methods-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 | $0.00104 | $0.01993 |
| Opus 5 | $0.00052 | $0.00996 |
| Sonnet 5 | $0.00021 | $0.00399 |
| Haiku 4.5 | $0.00010 | $0.00199 |
Grade A, and why
ta-methods-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 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TA 研究方法章节写作
自动从项目已有文件中提取已知信息,补问无法推断的细节,生成完整的研究方法章节草稿。 研究者只需确认和修改,无需从零描述。
第一步:自动读取项目信息
启动后,立即读取当前项目目录中的以下文件(如存在):
| 文件 | 可提取的信息 |
|---|---|
framework_[主题].md |
理论定位(A/B/C)、理论取向说明 |
coding_[被访者].md × N |
访谈份数、被访者编号/简称、研究主题 |
coding_GT_[被访者].md × N |
同上(GT编码文件) |
| 主题汇总表(如有) | 最终主题数量 |
读取完成后,输出信息确认清单:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
从项目文件中自动识别到以下信息:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✅ 研究主题:[从文件中提取]
✅ 分析方法:主题分析(Braun & Clarke 反思性 TA)
✅ 访谈份数:[N] 份(文件:coding_A.md, coding_B.md, ...)
✅ 理论定位:[A/B/C] — [说明]
✅ 最终主题数:[N] 个(如可从文件中识别)
⚠️ 以下信息无法从文件推断,需要你补充:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
第二步:补问无法推断的信息
仅询问文件中无法获得的必要信息,不重复询问已知内容:
请补充以下研究方法的关键细节:
研究对象的选择标准:你用什么标准选择被访者?(如:从事平台经济工作满1年、具有自主接单经历等)
抽样策略:如何找到这些被访者?(如:目的性抽样/滚雪球抽样/理论抽样,通过何种渠道联系)
数据收集时间与形式:
- 访谈在什么时间段进行?(如:2023年9月—2024年1月)
- 访谈形式?(面对面/线上视频/电话)
- 每次访谈时长大约?(如:60–90分钟)
- 是否录音?是否转录?
被访者基本情况(可选,用于描述样本):
- 性别构成?年龄范围?其他相关人口学特征?
- 是否达到理论饱和?
研究伦理:
- 是否获得伦理委员会批准?(如有,简述)
- 如何处理知情同意?
- 如何保护被访者隐私?(如:匿名化处理方式)
研究者定位(可选):
- 你与研究情境/被访者的关系?(局内人/局外人)
- 有无需要说明的利益关系或立场?
研究者可以简短回答,细节由 skill 补充完善为学术表述。
第三步:生成研究方法章节草稿
整合自动读取的信息与研究者补充的细节,生成完整研究方法章节。
章节结构(可根据目标期刊要求调整):
一、研究设计
说明为什么采用定性研究,为什么选择主题分析:
- 研究问题的性质(探索性/解释性/描述性)决定了定性取向的必要性
- 主题分析(Braun & Clarke,[年份])作为具体方法的适配性说明
- 根据理论定位(A/B/C)说明分析取向:
- A → 理论驱动的主题分析,编码在[理论]框架内进行
- B → 归纳性主题分析,主题从资料中自然涌现
- C → 以[理论概念]为敏感性概念的主题分析
写作要求:
- 必须说明"为什么是主题分析而不是其他方法"(如扎根理论、话语分析等),不能只描述方法步骤
- 引用 Braun & Clarke 时使用准确版本(2006年原版或2022年《反思性主题分析》)
二、研究对象与抽样
- 研究对象的界定(谁是本研究关注的群体)
- 纳入标准(选择被访者的依据)
- 抽样策略(如何找到并选择具体个案)
- 样本概况表(如适用):被访者编号、基本特征、访谈时长
写作要求:
- 说明抽样的理论依据,不能只说"随机选取"或"方便抽样"
- 如采用理论抽样,说明抽样如何随分析进展而调整
三、数据收集
- 访谈类型(半结构化/深度访谈)及其适用理由
- 数据收集时间段与地点/形式
- 访谈流程:如何开展、主要议题方向(不暴露具体问题,以免影响重复性)
- 录音与转录说明
四、数据分析
按 Braun & Clarke 主题分析六步法描述分析过程(但不是机械罗列步骤,而是说明在本研究中如何实施):
- 熟悉数据(反复阅读转录文本,记录初步想法)
- 生成初始编码(逐句编码,关注[研究主题]相关内容)
- 搜寻主题(将编码归并为候选主题)
- 审查主题(检查主题内部一致性与主题间差异性)
- 定义和命名主题(确定每个主题的核心含义)
- 撰写报告(将主题转化为叙事分析)
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 · 185 lines · 104 tokens per session scan A b69e76349ffd
ta-methods-writer is a skill published in the GitHub repository yipng05-max/-skills (285 stars, last pushed 4mo ago), licensed MIT. It adds 104 tokens to every session and 1,993 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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