Development
planning - Claude MCP Skill
Use when starting a non-trivial feature, refactor, or multi-file change. Forces structured design thinking before writing any code - brainstorm approaches, get approval, then create a step-by-step implementation plan.
SEO Guide: Enhance your AI agent with the planning tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to use when starting a non-trivial feature, refactor, or multi-file change. forces structured design th... Download and configure this skill to unlock new capabilities for your AI workflow.
Documentation
SKILL.md# Planning - Design Before Code Structured workflow for planning implementation before writing code. Prevents wasted effort from diving in without a clear direction. ## When to Use - New features spanning multiple files or modules - Refactors touching backend, frontend, or ingestion layers - Bug fixes where the root cause is unclear - Any task where the approach isn't immediately obvious ## Workflow ### Phase 1: Understand the Problem 1. **Read before suggesting.** Explore the relevant code, schemas, and tests. Do not propose changes to code you haven't read. 2. **Ask clarifying questions** one at a time. Don't dump a list of 10 questions. 3. **Identify constraints:** - Which layers are affected? (Java backend, React frontend, Python ingestion, JSON schemas) - Are there existing patterns to follow? (Check similar implementations) - Database migration needed? (Flyway in `bootstrap/sql/migrations/`) - Schema changes needed? (`openmetadata-spec/` JSON schemas) ### Phase 2: Propose Approaches Present **2-3 approaches** with trade-offs: ``` ## Approach A: [Name] - How it works: [1-2 sentences] - Pros: [bullet list] - Cons: [bullet list] - Files affected: [list] - Risk: [low/medium/high] ## Approach B: [Name] ... ## Recommendation: [A or B] because [reason] ``` **Wait for user approval before proceeding.** ### Phase 3: Create Implementation Plan Once an approach is approved, break it into ordered tasks: 1. Each task should be **completable in a single focused step** 2. Each task must specify: - Exact file paths to create or modify - What changes to make (not vague descriptions) - Verification command (test to run, build to check) 3. Order tasks by dependency — schema changes before model generation, backend before frontend 4. Include a final verification task that runs all relevant tests **OpenMetadata task ordering pattern:** ``` 1. JSON Schema changes (openmetadata-spec/) 2. Run: make generate (regenerate Pydantic models) 3. Java backend changes (openmetadata-service/) 4. Run: mvn spotless:apply && mvn test-compile 5. Python ingestion changes (ingestion/) 6. Run: cd ingestion && make py_format && make py_format_check && make unit_ingestion_dev_env 7. Frontend changes (openmetadata-ui/.../ui/) 8. Run: yarn lint && yarn test 9. Database migrations if needed (bootstrap/sql/) 10. Full verification: mvn test or relevant integration tests ``` ### Phase 4: Execute - Work through the plan task by task - Mark each task complete as you finish it - If you hit a blocker, stop and discuss — don't silently deviate from the plan - After all tasks complete, run the final verification ## Rules - **Never skip Phase 2.** Even if the approach seems obvious, stating it gets alignment. - **No placeholder code.** Every step in the plan must describe real, complete changes. - **Schema-first.** If the feature touches data models, start with JSON Schema changes in `openmetadata-spec/`. - **User approves before code.** Don't write code until the plan is approved.
Signals
Information
- Repository
- open-metadata/OpenMetadata
- Author
- open-metadata
- Last Sync
- 5/9/2026
- Repo Updated
- 5/9/2026
- Created
- 4/1/2026
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