Generate detailed specifications from Claude Code session logs. Ideal for developers and operations teams to document coding processes. Connects to Claude Code sessions, outputs structured specifications.
git clone https://github.com/marmelab/vibe-spec.gitVibe Spec solves the problem of scattered requirements in AI-assisted development by parsing Claude Code conversation logs and generating cohesive specifications. The tool reads iterative exchanges between developers and AI assistants, reconstructing a single source of truth that documents what the application should do. It's designed for developers and operations teams who need to document coding processes, maintain project clarity, and create structured specifications from incremental feature requests and refinements captured in session logs.
Install globally with `npm install -g vibe-spec`. Set up your OpenAI API key via environment variable or .env file. Run `vibe-spec spec` in your project directory to generate specifications, or use `vibe-spec logs` to parse and export conversation history with filtering options.
Document iterative AI-assisted development into structured specifications
Create single source of truth from Claude Code conversation logs
Generate project scope documentation from incremental feature requests
Export conversation logs to JSON for analysis and archival
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/marmelab/vibe-specCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Analyze the following Claude Code session logs and generate a detailed specification document. Focus on the [COMPANY] project named [PROJECT_NAME] in the [INDUSTRY] sector. Include all relevant technical details, dependencies, and workflow steps. Ensure the output is structured and easy to follow.
# Project Specification: [PROJECT_NAME] ## Overview - **Company**: [COMPANY] - **Industry**: [INDUSTRY] - **Project Lead**: [PROJECT_LEAD] - **Start Date**: [START_DATE] - **Completion Date**: [COMPLETION_DATE] ## Technical Details ### Dependencies - **Language**: Python 3.9 - **Frameworks**: Django 3.2, React 17 - **Database**: PostgreSQL 12 - **Cloud Services**: AWS EC2, S3 ### Workflow Steps 1. **Initial Setup**: - Configured development environment with virtualenv. - Installed necessary dependencies using pip. 2. **Database Schema Design**: - Designed relational schema for user management and product catalog. - Implemented migrations using Django's migration framework. 3. **API Development**: - Developed RESTful APIs for user authentication and product management. - Implemented JWT-based authentication. 4. **Frontend Integration**: - Integrated React frontend with Django backend. - Implemented responsive design using Bootstrap. 5. **Testing and Deployment**: - Conducted unit and integration testing. - Deployed application to AWS EC2 instances. ## Additional Notes - **Challenges**: - Initial issues with database connection pooling resolved by optimizing connection settings. - Frontend performance improved by implementing lazy loading for product images. - **Future Enhancements**: - Plan to integrate payment gateway for e-commerce functionality. - Consider implementing caching layer for frequently accessed data.
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