A specialized skill for writing body-only lead blocks that unify H2 chapters by connecting multiple H3 subsections into a single, cohesive argument.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chapter-lead-writer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install chapter-lead-writer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Chapter Lead Writer?
The Chapter Lead Writer is an essential tool within the Openclaw Skills library designed to solve the problem of fragmented long-form content. When an H2 section contains multiple H3 subsections, the narrative often feels like isolated paragraph islands. This skill synthesizes the chapter's core comparison lens and throughline, creating a bridge that previews the content without introducing new facts or citations.
Operating in a compatibility-preserving migration phase, the skill leverages structured assets and reference guides to ensure technical accuracy and stylistic consistency. It is specifically engineered to avoid common AI writing pitfalls such as slide narration, planner talk, and repetitive stock openers, ensuring a professional and high-level evaluation lens is maintained throughout the document.
Chapter Lead Writer Use Cases
- Harmonizing fragmented H2 chapters that contain multiple H3 subsections to improve reading flow.
- Establishing a clear comparison lens or throughline at the start of a complex technical chapter.
- Bridging distinct subsections into a single cohesive argument without adding external information.
- Replacing robotic table-of-contents narration with analytical and context-aware introductory prose.
How Chapter Lead Writer Works
- Eligibility Check: The skill scans outline/outline.yml to identify H2 sections with H3 subsections and verifies the existence of chapter briefs.
- Context Loading: It reads reference guides for lead block archetypes and throughline patterns to determine the best narrative frame.
- Citation Validation: The skill checks the existing citation scope against citations/ref.bib and optional context packs to ensure no out-of-scope facts are introduced.
- Execution: Using a compatibility-mode Python script, it generates a body-only Markdown file (sections/S
_lead.md) based on selected archetypes (lens-first, sequence-first, or comparison-first). - Contract Verification: The output is validated against the lead block contract to ensure it contains no headings and adheres to the strict no-new-facts guardrail.
Chapter Lead Writer Setup
To use this skill, ensure you have python3 installed and the required directory structure in your workspace.
# Navigate to your Openclaw Skills workspace
cd your-workspace-dir
# Run the lead writer for the entire workspace
python scripts/run.py --workspace .
# Target a specific unit for lead generation
python scripts/run.py --workspace . --unit-id chapter_01
Chapter Lead Writer Data Schema & Taxonomy
The skill relies on a structured input-output model to maintain document integrity:
| File Type | Path | Purpose |
|---|---|---|
| Input | outline/outline.yml |
Maps H2 and H3 hierarchy |
| Input | outline/chapter_briefs.jsonl |
Source of the chapter's intent and throughline |
| Input | citations/ref.bib |
Master bibliography for citation validation |
| Reference | references/lead_block_archetypes.md |
Templates for lens-first or sequence-first leads |
| Output | sections/S<sec_id>_lead.md |
The generated body-only lead block |
| Asset | assets/lead_block_contract.json |
Schema for output shape and validation |
Chapter Lead Writer Advanced Features
- Multi-Archetype Selection: Automatically chooses between lens-first, sequence-first, or comparison-first frames to avoid repetitive cadence.
- Compatibility Mode Execution: Uses a stabilized script path to ensure consistent behavior during architectural migrations.
- Cross-Cutting Citation Support: Integrates with writer_context_packs.jsonl for advanced, multi-chapter citation logic.
- Automated Anti-Pattern Filtering: Actively removes planner talk, slide narration, and count-based openers through reference-guided validation.
SKILL.md
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