tdd-verification-and-adversarial-review
Verify pre-written TDD tests pass, conduct adversarial code review on committed diffs, and route findings to existing issues
Best use case
tdd-verification-and-adversarial-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Verify pre-written TDD tests pass, conduct adversarial code review on committed diffs, and route findings to existing issues
Teams using tdd-verification-and-adversarial-review should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tdd-verification-and-adversarial-review/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tdd-verification-and-adversarial-review Compares
| Feature / Agent | tdd-verification-and-adversarial-review | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Verify pre-written TDD tests pass, conduct adversarial code review on committed diffs, and route findings to existing issues
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# TDD Verification & Adversarial Review When inheriting a TDD task where tests were pre-written: (1) Run full test suite to confirm RED→GREEN state and all 496+ tests pass; (2) Examine committed diffs for design choices (quantile logic, boundary conditions, type consistency); (3) Classify findings as MINOR (type annotation, docstring gaps) vs MAJOR (logic errors requiring fix round); (4) Route learnings to existing issues (#1972 for test-coverage gaps) rather than creating noise; (5) Post GitHub comment with verdict and push summary before exit.
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