A professional three-stage framework for collaborative document creation, guiding users from initial context gathering to rigorous reader testing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install doc-coauthoring-anthropic
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 doc-coauthoring-anthropic 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 Doc Co-Authoring Workflow?
The Doc Co-Authoring workflow is a sophisticated framework designed to transform AI agents into active editorial partners. Rather than simple text generation, this skill implements a structured methodology for creating technical specifications, proposals, and decision documents. By leveraging Openclaw Skills, developers can ensure their documentation is not only well-written but also battle-tested against ambiguity and context gaps.
This skill focuses on three distinct phases: Context Gathering, Refinement and Structure, and Reader Testing. This approach ensures that the resulting content is deeply rooted in project reality and optimized for clarity, making it an essential tool for high-stakes technical communication and organizational knowledge management.
Doc Co-Authoring Workflow Use Cases
- Drafting complex technical specifications or RFCs that require deep architectural context
- Developing Product Requirement Documents (PRDs) through iterative stakeholder feedback
- Creating decision logs and architectural proposals that need to withstand external scrutiny
- Refining existing documentation to remove 'slop' and ensure every sentence provides value
How Doc Co-Authoring Workflow Works
- Context Gathering: The agent initiates a meta-context discovery phase, asking about audience, impact, and constraints while pulling data from integrated sources.
- Information Synthesis: Users provide unstructured info dumps or link to external channels, which the agent organizes into a coherent knowledge base.
- Structural Scaffolding: The agent creates a document artifact or file with section placeholders to establish a clear roadmap.
- Iterative Development: Each section is built via a cycle of brainstorming, user curation, and surgical drafting using precise editing tools.
- Gap Analysis: The agent performs a final coherence check to identify contradictions, redundancies, or generic filler across the entire document.
- Reader Testing: The completed document is tested against a 'fresh' agent instance to identify blind spots and ensure the content is self-explanatory.
Doc Co-Authoring Workflow Setup
To deploy this documentation workflow, integrate the markdown definition into your agent's skill directory. Ensure your environment supports file manipulation and artifact management.
# Example of adding the skill to your local agent configuration
mkdir -p ~/.openclaw/skills
cp doc-coauthoring.md ~/.openclaw/skills/
For optimal performance, enable MCP servers or connectors for platforms like Slack, Google Drive, or SharePoint. These integrations allow Openclaw Skills to pull real-time context directly into the authoring process.
Doc Co-Authoring Workflow Data Schema & Taxonomy
The skill manages documentation data through a tiered structure that prioritizes iterative refinement over full re-writes.
| Component | Format | Purpose |
|---|---|---|
| Meta-Context | Structured Metadata | Defines audience, goals, and document constraints |
| Context Repository | Unstructured / Linked Data | Stores raw info dumps and external channel summaries |
| Working Draft | Markdown Artifact | The live document managed via surgical string replacements |
| Quality Metrics | Validation Log | Tracks reader testing results and identified blind spots |
Doc Co-Authoring Workflow Advanced Features
- Multi-agent verification: Uses sub-agents to simulate a reader with zero prior context to catch logical leaps.
- Surgical refinement: Utilizes specific string replacement functions to edit documents without reprinting or corrupting existing structure.
- Integration-aware context: Seamlessly pulls data from team communication tools and document storage to minimize manual input.
- Automated image accessibility: Proactively identifies missing alt-text and assists in generating descriptions for better AI readability.
SKILL.md
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