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Chapter Briefs

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WILLOSCAR
chapter-briefs

Build per-chapter (H2) writing briefs (NO PROSE) so the final survey reads like a paper (chapter leads + cross-H3 coherence) without inflating the ToC.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namechapter-briefs
Stars
513
Forks
39
Bundled files
1
Links
Skill sourceRepository
  • 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.

  • 1 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 Briefs 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-briefs
  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-briefs .claude/skills/chapter-briefs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chapter Briefs 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 Briefs 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 Briefs 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 Briefs (H2 writing cards) [NO PROSE]

Triggers & routing

  • Trigger: chapter briefs, H2 briefs, chapter lead plan, section intent, 章节意图, 章节导读, H2 卡片.
  • Use when: outline/outline.yml + outline/subsection_briefs.jsonl exist and you want thicker chapters (fewer headings, more logic).

Purpose: turn each H2 chapter that contains H3 subsections into a chapter-level writing card so the writer can:

  • add a chapter lead paragraph block (coherence)
  • keep a consistent comparison axis across the chapter
  • avoid “8 small islands” where every H3 restarts from scratch

This artifact is internal intent, not reader-facing prose.

Why this matters for writing quality:

  • Chapter briefs prevent the "paragraph island" failure mode: without a throughline, each H3 restarts and repeats openers.
  • Treat throughline and lead_paragraph_plan as decision constraints, not copyable sentences.

Inputs

  • outline/outline.yml
  • outline/subsection_briefs.jsonl
  • Optional: GOAL.md

Outputs

  • outline/chapter_briefs.jsonl

Output format (outline/chapter_briefs.jsonl)

JSONL (one object per H2 chapter that has H3 subsections).

Required fields:

  • section_id, section_title
  • subsections (list of {sub_id,title} in outline order)
  • synthesis_mode (one of: clusters, timeline, tradeoff_matrix, case_study, tension_resolution)
  • synthesis_preview (1–2 bullets; how the chapter will synthesize across H3 without template-y “Taken together…”)
  • throughline (3–6 bullets)
  • key_contrasts (2–6 bullets; pull from each H3 contrast_hook when available)
  • lead_paragraph_plan (2–3 bullets; plan only, not prose)
    • Each bullet should be chapter-specific and mention concrete handles (axes / contrast hooks / evaluation lens).
    • Avoid generic glue like "Para 1: introduce the chapter" without naming what is being compared.
  • bridge_terms (5–12 tokens; union of H3 bridge terms)

How C5 uses this (chapter lead contract)

The writer uses outline/chapter_briefs.jsonl to draft sections/S<sec_id>_lead.md (body-only; no headings).

Contract (paper-like, no new facts):

  • Preview the chapter’s comparison axes (2–3) and how the H3s connect; do not restate the table of contents.
  • Reuse key_contrasts / bridge_terms as handles (not templates) so the chapter reads coherent without repeating "Taken together" everywhere.
  • Keep it grounded (>=2 citations later in C5; do not invent new papers here).

Workflow

  1. (Optional) Read GOAL.md to pin scope/audience, and inject that constraint into the chapter throughline.
  2. Read outline/outline.yml and list H2 chapters that have H3 subsections.
  3. Read outline/subsection_briefs.jsonl and group briefs by section_id.
  4. For each chapter, produce:
    • a throughline: what the whole chapter is trying to compare/explain
    • key contrasts: 2–6 contrasts that span multiple H3s
    • a synthesis_mode: enforce synthesis diversity across chapters (avoid repeating the same closing paragraph shape)
    • a lead paragraph plan: 2–3 paragraph objectives (what the chapter lead must do)
    • a bridge_terms set to keep terminology stable across H3s
  5. Write outline/chapter_briefs.jsonl.

Quality checklist

  • One record per H2-with-H3 chapter.
  • No placeholders (TODO/…/(placeholder)/template instructions).
  • throughline and key_contrasts are chapter-specific (not copy/paste generic).
  • lead_paragraph_plan bullets explicitly preview 2–3 comparison axes and how the H3 subsections partition them (no generic chapter-intro boilerplate).

Script

Quick Start

  • uv run python .codex/skills/chapter-briefs/scripts/run.py --help
  • uv run python .codex/skills/chapter-briefs/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir>
  • --unit-id <U###>
  • --inputs <semicolon-separated>
  • --outputs <semicolon-separated>
  • --checkpoint <C#>

Examples

  • Default IO:
    • uv run python .codex/skills/chapter-briefs/scripts/run.py --workspace <workspace>
  • Explicit IO:
    • uv run python .codex/skills/chapter-briefs/scripts/run.py --workspace <workspace> --inputs "outline/outline.yml;outline/subsection_briefs.jsonl;GOAL.md" --outputs "outline/chapter_briefs.jsonl"

Refinement marker (recommended; prevents churn)

When you are satisfied with chapter briefs, create:

  • outline/chapter_briefs.refined.ok

This is an explicit "I reviewed/refined this" signal:

  • prevents scripts from regenerating and undoing your work
  • (in strict runs) can be used as a completion signal to avoid silently accepting a bootstrap scaffold

Notes

  • This helper is a bootstrap; refine manually if needed.

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 Briefs AI skill do?

Build per-chapter (H2) writing briefs (NO PROSE) so the final survey reads like a paper (chapter leads + cross-H3 coherence) without inflating the ToC.

Why use Chapter Briefs on TypingMind?

Because you install it once and use it with any model. Chapter Briefs 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 Briefs 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-briefs. 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 Briefs?

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 Briefs?

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

Is the Chapter Briefs 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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