A diagnostic tool that evaluates whether a project can be autonomously built by AI coding agents using the Vibe Coding methodology.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install vibe-coding-checker
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 vibe-coding-checker using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Vibe Coding Checker is a strategic evaluation tool designed for developers and creators within the Openclaw Skills ecosystem. It provides a technical feasibility analysis to determine if a project idea can be independently realized using modern AI coding agents like Cursor, Windsurf, or Bolt. By bridgeing the gap between a high-level idea and technical execution, it helps users understand the limitations and potential of automated development workflows.
This skill analyzes project descriptions against multiple dimensions, including technical complexity and context constraints, ensuring that users can make informed decisions before starting their build. As part of the Openclaw Skills collection, it empowers developers to leverage the full power of Vibe Coding by providing a clear implementation roadmap and risk assessment.
To get started with this assessment tool in your environment, follow these steps:
# Navigate to your local skills directory
cd your-openclaw-path/skills
# Run the evaluation script with your project idea
python3 scripts/evaluate_vibe.py --idea "Create a Chrome extension for social media analysis"
Ensure your local environment is correctly configured to interface with the Openclaw Skills framework for accurate results.
The Vibe Coding Checker organizes its assessment data into a structured output format for easy consumption:
| Section | Data Type | Description |
|---|---|---|
| Conclusion | Status Indicator | Feasibility level (Feasible, Partial, or Not Recommended) |
| Toolset | Array | List of recommended primary and auxiliary AI tools |
| Roadmap | List | Step-by-step breakdown of the implementation path |
| Risk Report | Markdown | Identified technical bottlenecks and boundary cases |
| Prompt Strategy | String | Custom prompt templates to guide AI agents through the build |
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