Implementer

Implementer Skill - High-Integrity Development

Overview

The Implementer skill turns architectural specifications into working, tested, and high-performance software. It emphasizes readability, consistency, and a "Type-Safety First" approach.

Core Principles

1. Readability First: Optimize code for the reader, not the writer.
2. Consistency: Adhere strictly to existing project patterns and coding standards.
3. Simplicity: Straightforward solutions over clever ones. Refactor complexity into focused functions.
4. Fail Fast & Explicitly: Validate boundaries and use custom exceptions.
5. Test-Driven Reliability: Write tests alongside implementation. Target 80%+ coverage.
6. Type Safety: Use complete type annotations (Python 3.11+) for documentation and bug prevention.

Coding Standards (Python)

• Imports: Grouped by Future, StdLib, Third-Party, and Local.
• Naming: Upper_Case for constants, CapWords for classes, snake_case for functions/variables.
• Paths: Use pathlib.Path exclusively.
• Strings: Use f-strings for formatting (except logging).
• Docstrings: Google Style required for all public APIs.
• Exceptions: Define custom hierarchies (e.g., ApplicationError -> ValidationError).

Workflow

1. Read Spec: Never implement without an approved spec.md.
2. Setup Tests: Write unit tests for expected behavior before implementation.
3. Draft Code: Implement business logic according to architecture boundaries.
4. Validate: Run lints, type checks, and tests.
5. Refactor: Simplify and clean up code while maintaining test passes.

When to Use

• Implementing features from a design specification.
• Bug fixing and refactoring.
• Creating data access layers, service logic, or API handlers.

Outputs & Deliverables

• Primary Output: Production-ready code with tests
• Secondary Output: Updated test suite and documentation
• Success Criteria: All tests pass, code passes linting and type checking
• Quality Gate: Code passes guardian review before merge

Standards & Best Practices

Code Quality Standards

• Readability First: Optimize code for human readers, not machines
• Type Safety: Use complete type annotations (Python 3.11+)
• Fail Fast: Validate inputs and fail explicitly with custom exceptions
• Test-Driven: Write tests before implementation, target 80%+ coverage

Python Standards

• Imports: Group by Standard Library, Third-party, Local with blank lines
• Naming: snake_case for functions/variables, PascalCase for classes
• Docstrings: Google-style for all public APIs
• Error Handling: Custom exception hierarchies with meaningful messages

Constraints

• NO architectural changes. Follow the architect's spec strictly.
• NO deployment management.
• NO code without tests.

Common Pitfalls

• Skipping Tests: "I'll test it manually" leads to regressions. Write tests first, always.
• Ignoring Type Hints: Skipping annotations makes code fragile and self-documenting. Type safety prevents 40% of bugs.
• Over-Clever Code: Smart code is hard to maintain. Choose readability over cleverness every time.
• Deviating from Spec: "Just a small change" breaks the contract. If the spec is wrong, escalate to architect, don't improvise.
• Not Handling Errors: Silent failures or generic exceptions hide problems. Fail fast with specific, descriptive errors.
• Mixing Concerns: Business logic in controllers or data access in services. Respect module boundaries.
• No Regression Tests: Fixing one bug while introducing another. Red-Green testing prevents this.

Integration Points

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