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 introduction-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/introduction-writer)<a href="https://agentmods.dev/skills/yipng05-max/-skills/introduction-writer"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/introduction-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/introduction-writer"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/introduction-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 | $0.01326 |
| Opus 5 | $0.00046 | $0.00663 |
| Sonnet 5 | $0.00019 | $0.00265 |
| Haiku 4.5 | $0.00009 | $0.00133 |
Grade A, and why
introduction-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 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.
What it actually says
学术论文引言写作
为定性社会学研究生成引言章节。引言是论文的入口,任务是让读者在进入正文前就明白: 研究的问题是什么、为什么值得研究、既有研究在哪里停下来了、本研究如何接续。
启动:收集必要信息
启动时收集以下信息(在 ta-research-workflow 中已有的信息自动复用,不重复询问):
- 研究问题(已聚焦的版本)
- 研究背景与现实动因:是什么现实现象或社会变化让这个问题变得重要?
- 研究空白:既有研究在哪里停下来了?(可从文献综述输出中提取,或研究者直接描述)
- 目标期刊类型:C刊 / SSCI(影响篇幅与风格)
- 可选:预期主要发现或贡献(若已知,可在引言中提前简述)
写作流程
第一步:诊断研究问题的性质
在写作前,先判断研究问题的性质,这决定引言的叙事策略:
-
现象驱动型:研究从一个具体的、可观察的现象出发(如某个社会变化、某类群体的行为),问题是"这个现象是怎么发生的/意味着什么" → 引言策略:先呈现现象的具体性和重要性,再导向理论空白
-
理论驱动型:研究从理论争论出发,数据是为了检验或修正某个命题 → 引言策略:先锚定理论对话,再说明为什么需要新的经验研究
-
方法驱动型:研究针对既有研究的方法局限(如缺乏质性视角、缺乏某类情境的研究) → 引言策略:先呈现既有研究的方法局限,再说明本研究如何补充
判断后,按对应策略生成引言。
第二步:生成引言正文
结构(共5个功能段,不是5个机械段落,可根据需要合并或拆分):
功能段 1:锚定问题
- 用1–2段将读者带入研究情境
- 呈现具体的现象、数据、或理论困境——让读者感受到"这里有一个真实的问题"
- 禁止套话开头:不能用"在当今社会""随着XX的发展""在全球化背景下"
- 开头句必须有实质内容:一个具体现象、一个反直觉的事实、一个理论张力
功能段 2:文献脉络与不足
- 简述既有研究如何处理这个问题(2–4句,不展开,综述在后面的章节)
- 从文献脉络中自然导出研究空白:不是宣称"目前研究不足",而是展示"已有研究到此为止,留下了这个问题没有回答"
- 研究空白的表述必须具体:是哪类研究情境缺失、哪个机制未被探明、哪个群体被忽略
功能段 3:本研究的切入
- 明确说明本研究研究什么、在哪里研究、用什么方法
- 研究问题用一句话清晰陈述(不绕弯)
- 说明研究设计如何针对性地填补上述空白
功能段 4:研究贡献
- 简述本研究对学科知识的贡献
- 贡献必须具体指向理论对话:不能只说"丰富了XX研究""拓展了XX的边界""为XX提供了参考"
- 正确表述:本研究通过[具体发现],[修正/扩展/挑战/验证]了[具体理论概念或命题]
- 贡献陈述数量:1–2条,不宜罗列
功能段 5:论文结构
- 简述各章节内容(1段,每章1句话)
- 此段可在篇幅紧张时压缩或省略(SSCI期刊论文有时不设此段)
第三步:质量自检
写完后输出自检报告:
引言自检:
□ 开头句是否有实质内容(无套话)?
□ 研究空白是否从文献脉络自然导出(非生硬宣称)?
□ 研究问题是否清晰、一句话可陈述?
□ 贡献表述是否具体指向理论对话?
□ 全文是否有冗余的过渡句或修饰语?
如有不符合项,自动修订后再输出最终版本。
篇幅参考
- C刊:800–1200字(不含论文结构段则800–1000字)
- SSCI:500–800词(英文),结构更紧凑,贡献表述更直接
输出与保存
写作完成后,调用 Write 工具保存:
文件命名:introduction.md
保存路径:当前项目目录
告知研究者:
"引言已保存至
introduction.md([字数]字)。 如在 ta-research-workflow 中,请继续下一检查点。"
语言
- 默认中文
- 如目标期刊为SSCI英文期刊,询问是否需要英文版本
- 中文写作避免八股文风,书面化但不僵化
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 · 117 lines · 93 tokens per session scan A 1a8daa3a70ce
introduction-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 1,326 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.
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…