What problem does it solve? Writing tests directly from intuition leads to coverage biased toward happy paths, with boundary and error cases silently missed. This Skill enforces an upfront extraction phase that enumerates every behavior worth testing into a traceable ledger before any test is written. ## Core Features & Use Cases - Exhaustive behavior extraction: Scans a feature, module, API, or PR diff using anchors like acceptance criteria, function signatures, branches, and state transitions, then records each behavior as a numbered T-ID entry in tasks/test-design/<target>.md. - EARS-based classification: Labels each behavior as event, state, ubiquitous, unwanted, optional, boundary, or nonfunctional, forcing at least one abnormal-case row per normal-case row. - Bidirectional test review: Maps existing tests against the ledger in both directions to find missing tests, unjustified tests, and skipped tests that are silently not running. - Use Case: Before implementing tests for a payment module, run the extraction to produce a T-ID ledger covering double-payment prevention, expired-deadline operations, and boundary conditions, then hand the closed ledger to the test-catalog step for technique assignment. ## Quick Start Ask the AI to extract all testable behaviors from the target feature into a T-ID ledger using the test-extract skill.