deliberation-tester

What problem does it solve?

Ensuring the reliability and correctness of complex AI deliberation features, adapters, and convergence logic requires a systematic testing approach. This skill teaches Test-Driven Development (TDD) patterns to build robust AI features with confidence, reducing bugs and improving code quality.

Core Features & Use Cases

• TDD Workflow: Master the red-green-refactor cycle for developing new AI features.
• Mocking Strategies: Effectively mock adapters and subprocesses for fast, isolated unit tests.
• HTTP Test Recording: Use VCR cassettes to record and replay HTTP responses, ensuring consistent and efficient integration tests for HTTP adapters.
• Use Case: When adding a new YourCLIAdapter, first write a unit test that expects parse_output to correctly extract the model's response from raw CLI output. Then, implement the adapter to make the test pass, ensuring robust parsing logic.

Quick Start

To begin, write a failing unit test in tests/unit/test_new_feature.py for your new deliberation feature. Then, run pytest tests/unit/test_new_feature.py -v to verify it fails as expected.