A specialized AI agent skill that transforms a single set of project facts into multiple tailored communication versions for executives, clients, and internal teams.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install stakeholder-update-drafter
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install stakeholder-update-drafter using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Stakeholder Update Drafter is a high-efficiency tool designed for complex communication workflows within Openclaw Skills. It solves the common pain point of manual report adaptation by taking raw project data—such as progress, risks, and next steps—and automatically drafting distinct versions optimized for different stakeholder perspectives.
This skill ensures professional transparency by maintaining a unified factual base while adjusting the tone and level of detail for executive summaries, client-facing milestones, and internal execution plans. It prioritizes integrity and risk transparency, refusing to exaggerate achievements or hide critical blockers.
To use this skill within the Openclaw Skills framework, ensure you have Python 3 installed. You can run the drafting script directly via the CLI:
# Run the update drafter with input and output files
python3 "{baseDir}/scripts/run.py" --input <input_file> --output <output_file>
If the shell environment is restricted, the skill will manually generate text based on the structure defined in {baseDir}/resources/spec.json.
The skill organizes its operations and outputs using a standardized local file structure:
| Resource | Path | Description |
|---|---|---|
| Specification | {baseDir}/resources/spec.json |
Defines the rules and logic for audience adaptation. |
| Templates | {baseDir}/resources/template.md |
Markdown templates for the various report versions. |
| Examples | {baseDir}/examples/ |
Reference inputs and outputs for different scenarios. |
| Testing | {baseDir}/tests/smoke-test.md |
Standard smoke tests to verify output quality. |
Loading
A zero-API-key search aggregator for AI agents specifically designed to retrieve and navigate Chinese web content and domestic search engines.

A sophisticated AI video generation skill that leverages the Wan model family through the Media.io OpenAPI to create cinematic 1080p content from text or images.

A comprehensive guide for AI agents to write, refactor, and review Python code with a focus on readability and maintainability.

A specialized tool for maintaining structural integrity and citation consistency in academic and technical manuscripts.

A proactive processing skill designed to log user corrections and agent errors to drive continuous self-improvement for AI systems.

A professional trade intelligence skill for querying UniProfit importer data, exhibition leads, and procurement requirements.








































