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chapter-lead-writer章节主要作者

Agent Skill

chapter-lead-writer 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

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下载量

1,451
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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/chapter-lead-writer
安装命令:
openclaw skills install 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。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install chapter-lead-writer

简介

编写 H2 章节引导块,预览本章比较视角并串联其下 H3 小节内容。

  • 适用于增强长文档可读性与逻辑流畅性的场景,尤其适合多源整合型写作任务。
  • 自动生成 sections/S<sec_id>_lead.md 文件,连接已有小节而不添加新功能。
  • 安装命令为 openclaw skills install chapter-lead-writer,需确保目标路径可写。
  • 引导内容基于现有材料推导,建议人工审阅以确保意图传达准确。

SKILL.md

name
chapter-lead-writer
description
Write H2 chapter lead blocks (sections/S<sec_id>_lead.md) that preview the chapter''s comparison lens and
version
0.1.0
metadata
openclaw
requires
anyBins

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 scripts/run.py --workspace <workspace_dir>

All Options

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

Examples

  • python 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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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按下载量换算1,107

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权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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