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Agent Skill · 研究检索

chapter-lead-writer(章节主要作者)

chapter-lead-writer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

chapter-lead-writer 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 47,223。

总安装 744周安装 31GitHub Stars 422下载 248核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:chapter-lead-writer(章节主要作者)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/chapter-lead-writer
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill chapter-lead-writer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill chapter-lead-writer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

chapter-lead-writer 用于查找、检索和筛选章节引导段落写作规范,适合在 Codex、Claude、Cursor、Gemini CLI 中为多小节章节撰写连贯引言。适用于需要将多个 H3 子部分整合为统一论证逻辑的文档编写场景。

使用时可结合安装命令和原始 README 继续核验内部结构与迁移状态,确保兼容性。安装前建议确认权限范围和维护状态,注意是否涉及内容生成或脚本执行。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/chapter-lead-writer

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/willoscar/research-units-pipeline-skills --ski…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

chapter-lead-writer 怎么安装?

在终端执行 npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill chapter…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

chapter-lead-writer 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

chapter-lead-writer 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

chapter-lead-writer 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

chapter-lead-writer 用的人多吗?

公开数据是累计安装 744 次,近一周安装 31 次,下载 248 次,GitHub 422 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

chapter-lead-writer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
chapter-lead-writer

章节主要作者 · 本页

744422只读MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Chapter Lead Writer

Purpose

This skill writes the body-only lead block that sits under an H2 heading and makes a chapter with multiple H3 subsections read like one argument.

This SKILL.md is now the package router, not the full method manual.

Migration status

This package is in P0 compatibility-preserving migration:

  • references/ and assets/ now hold the intended knowledge and contract layers.
  • scripts/run.py remains in compatibility mode for active generation.
  • a later script-thinning pass should move more judgment and exemplars out of Python and leave the script with deterministic execution and validation only.

For now, preserve the existing output contract and treat scripts/run.py as the execution source of truth.

Inputs

Required:

  • outline/outline.yml
  • outline/chapter_briefs.jsonl
  • citations/ref.bib

Optional:

  • outline/writer_context_packs.jsonl

Outputs

For each H2 section with H3 subsections:

  • sections/S<sec_id>_lead.md

Output contract

Keep these file-shape rules stable:

  • each lead file is body-only and contains no headings
  • each lead file previews the chapter lens and connects multiple H3s as one argument
  • each lead file stays within the chapter's existing citation scope
  • each lead file adds no new facts that are not supported later in the chapter

Load Order

Always read:

  • references/overview.md
  • references/lead_block_archetypes.md

Read by task:

  • references/throughline_patterns.md — when chapter briefs are thin or hard to convert into a throughline
  • references/bridge_examples.md — when the lead needs stronger H3 transitions without slide narration
  • references/bad_narration_examples.md — when removing table-of-contents narration, planner talk, count-based openers

Machine-readable assets:

  • assets/lead_block_contract.json — stable package contract for lead-block shape
  • assets/lead_block_compatibility_defaults.json — fallback phrasing, item limits, joiners, sentence cadence

Routing rules

Use this skill in the following order:

  1. Confirm the chapter is eligible
  • identify H2 sections with H3 subsections from outline/outline.yml
  • locate the corresponding chapter brief in outline/chapter_briefs.jsonl
  1. Load the method
  • read references/overview.md
  • read references/lead_block_archetypes.md
  • load the other reference files only if the chapter brief or current prose needs them
  1. Check citation scope
  • if outline/writer_context_packs.jsonl exists, use it for cross-cutting chapter citations
  • keep any citations inside the existing chapter scope and validate keys against citations/ref.bib
  1. Execute
  • current phase: use scripts/run.py in compatibility mode to preserve active behavior and output shape
  • future phase: keep scripts/run.py for deterministic execution only, with the writing method and anti-pattern inventory living in references/

Compatibility mode note

scripts/run.py still contains active lead-generation logic.

That is temporary. For now:

  • do not treat the current script wording as the target architecture
  • do treat assets/lead_block_compatibility_defaults.json as the primary compatibility-mode wording source
  • do not copy large prose instructions back into SKILL.md
  • do preserve the current output contract while reducing obvious narration stems in the active path

What this skill should guarantee

Regardless of where the detailed method lives, this skill should produce chapter leads that:

  • state the chapter's comparison lens rather than narrating the outline
  • connect the H3 subsections as one argument, not as isolated stops on a tour
  • introduce recurring contrasts without slash-list jargon
  • keep the evaluation or calibration lens visible at a high level
  • avoid slide narration, planner talk, and repeated stock openers
  • choose from multiple candidate lead frames when possible (lens-first / sequence-first / comparison-first) and keep the least narrated option instead of reusing one stock cadence everywhere

Block conditions

Stop and route upstream if any of these are true:

  • outline/chapter_briefs.jsonl is missing
  • the target H2 section has no H3 subsections
  • the chapter brief is too incomplete to infer a throughline safely
  • the requested lead would require new facts or out-of-scope citations

Script role

scripts/run.py should currently be treated as a compatibility executor.

Its long-term role after script thinning is narrower:

  • chapter discovery
  • brief loading and normalization
  • contract validation
  • deterministic report writing

It is not the long-term home for lead archetypes, bridge examples, or narration anti-patterns.

Script

Quick Start

  • python.codex/skills/chapter-lead-writer/scripts/run.py --workspace <workspace_dir>

All Options

  • --workspace <dir>
  • --unit-id <id>
  • --inputs <a;b;...>
  • --outputs <a;b;...>
  • --checkpoint <C*>

Examples

  • python.codex/skills/chapter-lead-writer/scripts/run.py --workspace workspaces/<ws>

Troubleshooting

  • If outline/chapter_briefs.jsonl is missing or too thin, rebuild chapter briefs first.
  • If outline/writer_context_packs.jsonl is missing, the script will still run but with a thinner citation pool.
  • If a generated lead sounds narrated, patch the compatibility asset and references before changing Python.

平台分布

Claude Code

28.88%
按下载量换算72

Gemini CLI

23.59%
按下载量换算59

Cursor

19.08%
按下载量换算47

Codex

14.13%
按下载量换算35

OpenCode

8.91%
按下载量换算22

Antigravity

3.93%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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