gsd-adversarial-review-pattern
Catch hidden test failures by running adversarial review on sparse-data edge cases before final push
Best use case
gsd-adversarial-review-pattern is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Catch hidden test failures by running adversarial review on sparse-data edge cases before final push
Teams using gsd-adversarial-review-pattern 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/gsd-adversarial-review-pattern/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How gsd-adversarial-review-pattern Compares
| Feature / Agent | gsd-adversarial-review-pattern | 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?
Catch hidden test failures by running adversarial review on sparse-data edge cases before final push
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.
SKILL.md Source
# GSD Adversarial Review Pattern After implementing analytics functions that handle sparse or missing data, run a focused adversarial review on the exact diff before pushing. Specifically: (1) verify test assertions match the actual filter logic (not just fixture coincidence), (2) check for silent failures where data passes validation but shouldn't, (3) re-run full test suite after fixes. This catches TDD precision errors where tests accidentally pass on incomplete fixtures but would fail on real data.
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