bkit-claude-code

bkit transforms Claude Code into a context engineering system that guides AI-generated code through automated verification against design specifications. It eliminates guesswork by measuring code quality against your requirements, auto-repairing gaps up to five cycles, and halting workflows at 11 quality gates before drift compounds. The tool splits large projects into context-budgeted sprints that fit within Claude Code's session window, preserving progress and memory across interruptions. Built for first-time AI developers, solo builders, and team leads, bkit uses 44 skills and 34 specialist agents orchestrated by an AI tech lead to handle frontend, backend, QA, and security tasks in parallel. Every feature generates documentation (PRD, design, analysis, completion report) that serves as an audit trail.

1. **Define the workflow**: List the manual steps in [WORKFLOW_NAME] and identify the [SPECIFIC_PAIN_POINT] you want to automate. Use tools like Miro or Lucidchart to map the process visually. 2. **Set up Claude Code**: Install Claude Code in your project directory (`npm install -g @anthropic-ai/claude-code`) and ensure you have access to the required APIs (e.g., Auth0, Slack, Jira). Configure environment variables in a `.env` file. 3. **Generate the script**: Run the prompt template in your AI assistant, replacing placeholders with your workflow details. Review the generated `onboarding-automation.js` (or similar) for accuracy and security risks (e.g., hardcoded API keys). 4. **Test incrementally**: Use the Check phase to run the script with [TEST_DATA_SOURCE] (e.g., a CSV of synthetic users). Start with a small subset of data and scale up. Log output to [LOG_FILE] for debugging. 5. **Deploy and monitor**: Deploy the refined script to [DEPLOYMENT_TARGET] (e.g., AWS Lambda, GitHub Actions). Set up alerts for errors (e.g., Sentry for JavaScript errors) and monitor [METRICS] like completion rate and time saved. Iterate based on feedback.