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Chapter Lead Writer

Community
WILLOSCAR
chapter-lead-writer

Write H2 chapter lead blocks (`sections/S<sec_id>_lead.md`) that preview the chapter's comparison lens and connect its H3 subsections, without adding new facts.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namechapter-lead-writer
Stars
513
Forks
39
Bundled files
8
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Chapter Lead Writer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/chapter-lead-writer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/chapter-lead-writer .claude/skills/chapter-lead-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chapter Lead Writer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Chapter Lead Writer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Chapter Lead Writer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Chapter Lead Writer

Triggers & routing

  • Trigger: chapter lead writer, section lead writer, H2 lead, lead paragraph, 章节导读, 章节导语.
  • Use when: you have H2 chapters with multiple H3 subsections and the draft reads like paragraph islands across subsections.

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

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

All Options

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

Examples

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

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Chapter Lead Writer AI skill do?

Write H2 chapter lead blocks (`sections/S<sec_id>_lead.md`) that preview the chapter's comparison lens and connect its H3 subsections, without adding new facts.

Why use Chapter Lead Writer on TypingMind?

Because you install it once and use it with any model. Chapter Lead Writer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Chapter Lead Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/chapter-lead-writer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Chapter Lead Writer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Chapter Lead Writer?

As many as you like. As long as a model supports skills, you can use Chapter Lead Writer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Chapter Lead Writer AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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