The Agentic Startup is a collection of Claude Code commands, skills, and agents designed to accelerate software development. It benefits developers and operations teams by automating repetitive tasks, improving code quality, and speeding up deployment. It connects to Claude Code and integrates with existing development workflows.
git clone https://github.com/rsmdt/the-startup.gitThe Agentic Startup is a collection of Claude Code commands, skills, and agents designed to accelerate software development. It benefits developers and operations teams by automating repetitive tasks, improving code quality, and speeding up deployment. It connects to Claude Code and integrates with existing development workflows.
[{"step":"Identify the repetitive task or workflow bottleneck in your development process.","action":"Use Claude Code to analyze your project (e.g., `claude code analyze-repo --path /path/to/project`). Look for manual steps in CI/CD, testing, or deployment that could be automated.","tip":"Focus on tasks that are: (1) performed frequently, (2) prone to human error, or (3) time-consuming. Prioritize based on impact vs. effort."},{"step":"Customize the-startup skill for your project.","action":"Replace [PLACEHOLDERS] in the prompt template with your project-specific details (e.g., project name, module names, team workflows). For example: 'Use the-startup skill to automate the testing suite for [PROJECT_NAME] by generating a pytest configuration file with [SPECIFIC_GOAL: e.g., 'parallel test execution'].'","tip":"Use the `--dry-run` flag in Claude Code to preview changes before applying them. Example: `claude code --dry-run 'refactor the user service to follow clean architecture principles'`."},{"step":"Execute the automation using Claude Code.","action":"Run the command in your terminal. For example: `claude code 'use the-startup skill to generate a GitHub Actions workflow for [PROJECT_NAME] that includes [TESTING_FRAMEWORK] and [DEPLOYMENT_TARGET]'`.","tip":"Monitor the output for errors or warnings. Use `claude code --debug` to get detailed logs if something goes wrong."},{"step":"Test and iterate.","action":"Manually verify the automated process (e.g., trigger the CI/CD pipeline, run the refactored code). Use the `--test` flag in Claude Code to validate changes. Example: `claude code --test 'validate the new deployment script for [PROJECT_NAME]'`.","tip":"Gather feedback from your team on the new workflow. Adjust the automation based on their input (e.g., add error handling, improve logging)."},{"step":"Integrate into your existing workflow.","action":"Update documentation (e.g., README.md, team wikis) and train your team on the new process. Use the-startup skill to generate training materials or FAQs if needed.","tip":"Schedule a 30-minute team demo to walk through the automated workflow. Highlight time savings and reliability improvements."}]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/rsmdt/the-startupCopy 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.
Use the-startup skill to [TASK: e.g., 'automate the deployment pipeline for [PROJECT_NAME]', 'generate a pull request template for [TEAM_NAME]', or 'refactor the [MODULE_NAME] to follow best practices']. Focus on [SPECIFIC_GOAL: e.g., 'reducing manual steps', 'improving test coverage', or 'standardizing documentation']. Include [PLACEHOLDERS] for customization like project paths, team names, or module names.
### Automated Deployment Pipeline for 'Acme Analytics' **Context:** The Acme Analytics team requested an automated deployment pipeline to reduce manual errors and accelerate releases. The current process involves: - Manual Docker image builds (3 steps) - Manual Terraform deployments (2 steps) - Manual test suite execution (1 step) - Manual rollback procedures (if needed) **Solution Implemented:** 1. **CI/CD Pipeline:** Created a GitHub Actions workflow (`/.github/workflows/deploy.yml`) that: - Automatically builds Docker images on `main` branch pushes - Runs unit tests (`pytest`) and integration tests (`cypress`) - Deploys to staging via Terraform (`terraform apply -auto-approve`) - Sends Slack notifications to `#dev-deployments` on success/failure - Includes a manual approval step for production deployments 2. **Rollback Script:** Added a `rollback.sh` script in the `/scripts` directory that: - Reverts the last Terraform deployment - Restores the previous Docker image version - Notifies the team via Slack 3. **Documentation:** Updated the `README.md` with: - Step-by-step deployment instructions - Troubleshooting guide for common issues - Links to monitoring dashboards (Grafana) **Results:** - Deployment time reduced from **45 minutes** to **8 minutes** (82% improvement) - Manual errors eliminated (0 failed deployments in the last 2 weeks) - Team productivity increased by **30%** due to reduced context switching **Next Steps:** - Add automated performance testing to the pipeline - Implement blue-green deployment for zero-downtime releases - Integrate with Jira to auto-update ticket statuses post-deployment
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