Collection of 33 specialized AI subagents for Claude Code. Enhances full-stack development workflows with domain-specific expertise and automation. Connects to Claude Code for intelligent automation in operations.
git clone https://github.com/lst97/claude-code-sub-agents.githttps://docs.anthropic.com/en/docs/claude-code
[{"step":"Identify the task to automate","action":"Determine which part of your workflow (e.g., database migrations, API generation, testing) can be automated using claude-code-sub-agents. Refer to the [list of 33 subagents](https://github.com/your-repo/claude-code-sub-agents) to match the task to a subagent.","tip":"Start with repetitive tasks (e.g., schema updates, boilerplate code generation) to maximize time savings. Use the `list-subagents` command in Claude Code to explore available options."},{"step":"Set up the subagent","action":"Run the subagent with the required parameters. For example: `claude-code-sub-agents database-migrator --project-path /path/to/project --schema-file schema.sql`. Ensure your project is in a git repository to track changes.","tip":"Use the `--dry-run` flag to preview changes before applying them. Always commit your changes to a branch before merging to avoid disrupting the main branch."},{"step":"Integrate with existing tools","action":"Connect the subagent’s output to your workflow. For example, if the subagent generates a migration file, add it to your CI/CD pipeline (e.g., GitHub Actions, GitLab CI). If it generates code, run linters (ESLint, Prettier) and tests (Jest, pytest) to ensure quality.","tip":"Use tools like `husky` to run linters/formatters automatically before commits. For CI/CD, ensure the subagent’s output is included in the build steps."},{"step":"Review and iterate","action":"Manually review the generated output (e.g., migration files, API endpoints) in a staging environment. Test edge cases and update the subagent’s parameters if needed. For example, adjust the `api-endpoint-generator` to include additional fields or validation rules.","tip":"Use the `--verbose` flag to get detailed logs from the subagent. If the output isn’t perfect, tweak the subagent’s configuration or provide additional context (e.g., example API specs)."},{"step":"Automate the automation","action":"Once satisfied, add the subagent to your project’s automation scripts (e.g., `package.json` scripts, Makefile) to run it regularly. For example: `npm run migrate-db` or `make generate-api`.","tip":"Document the automation in your project’s README or wiki so team members can use it. Include examples of the subagent’s output and how to customize it."}]
Automatically delegate frontend development tasks to the appropriate UI specialists based on project requirements.
Streamline backend API design and implementation by invoking specialized backend architects for RESTful services.
Enhance code quality through automated code reviews and testing processes managed by dedicated QA experts.
Optimize cloud infrastructure and deployment processes with the help of deployment engineers and cloud architects.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/lst97/claude-code-sub-agentsCopy 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 claude-code-sub-agents to automate [TASK] in [PROJECT]. Focus on [SPECIFIC_DOMAIN] and apply [SUBAGENT_NAME] for [PURPOSE]. Ensure the solution integrates with [EXISTING_TOOLS] and follows [CODING_STANDARDS]. Example: 'Use claude-code-sub-agents to automate the deployment pipeline for my React e-commerce app. Focus on backend services and apply the 'database-migrator' subagent to handle schema updates. Ensure the solution integrates with GitHub Actions and follows REST API best practices.'
Here’s how the claude-code-sub-agents would automate a full-stack task for a fictional project: **Project Context**: A Node.js/React SaaS application with a PostgreSQL database and Dockerized deployment pipeline. **Task**: Automate the monthly database schema migration and API endpoint generation. **Subagents Used**: 1. **database-migrator**: Analyzed the current schema (`users`, `products`, `orders` tables) and generated a migration file (`2024_05_01_add_indexes.sql`) to add indexes to frequently queried columns (e.g., `user_email`, `order_date`). The subagent also updated the `migrations/metadata.json` to track the new migration. 2. **api-endpoint-generator**: Scanned the updated schema and auto-generated RESTful CRUD endpoints for the `products` table in `/src/routes/products.js`. The endpoints included: - `GET /api/products` (with pagination support) - `POST /api/products` (with validation for `name`, `price`, `stock` fields) - `PUT /api/products/:id` (with partial updates) - `DELETE /api/products/:id` **Integration**: The generated migration was automatically added to the CI/CD pipeline in `.github/workflows/deploy.yml`, ensuring it runs before the API deployment. The new endpoints were linted with ESLint and tested with Jest (mocking the database layer). **Output**: - Migration file: `migrations/2024_05_01_add_indexes.sql` - API routes: `/src/routes/products.js` (200 lines of auto-generated code) - Test coverage: 95% for the new endpoints (simulated with fictional data) - CI/CD update: Added `run-migrations` step to the deployment workflow. **Next Steps**: 1. Review the generated code in a PR (e.g., `feat/add-product-endpoints`). 2. Manually test the new endpoints in the staging environment. 3. Merge the PR and deploy to production. **Time Saved**: ~4 hours of manual work (schema analysis, endpoint writing, CI/CD updates).
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