adversarial-code-review-tdd
Systematic adversarial review pattern to identify breaking assumptions in already-passing test suites
Best use case
adversarial-code-review-tdd is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Systematic adversarial review pattern to identify breaking assumptions in already-passing test suites
Teams using adversarial-code-review-tdd 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/adversarial-code-review-tdd/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How adversarial-code-review-tdd Compares
| Feature / Agent | adversarial-code-review-tdd | 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?
Systematic adversarial review pattern to identify breaking assumptions in already-passing test suites
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.
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SKILL.md Source
# Adversarial Code Review for Passing Tests When tests pass but implementation may have hidden flaws, conduct adversarial review by: (1) identify sentinel values or default parameters that conflate different states (e.g., `decline_rate=0.0` as both "not set" and "valid input"), (2) check boundary conditions for unjustified constraints (e.g., `b_factor < 1` when b=1 is mathematically valid), (3) distinguish between user intent and implementation convenience. Fix MAJOR findings, add regression tests for edge cases, then re-run full suite before committing.
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