adversarial-code-review-for-committed-diffs
Systematic process for reviewing already-committed code changes to catch type inconsistencies, edge cases, and docstring gaps
Best use case
adversarial-code-review-for-committed-diffs is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Systematic process for reviewing already-committed code changes to catch type inconsistencies, edge cases, and docstring gaps
Teams using adversarial-code-review-for-committed-diffs 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-for-committed-diffs/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How adversarial-code-review-for-committed-diffs Compares
| Feature / Agent | adversarial-code-review-for-committed-diffs | 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 process for reviewing already-committed code changes to catch type inconsistencies, edge cases, and docstring gaps
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 Committed Diffs When code is already pushed and tests pass, perform targeted adversarial review: (1) Identify design choices (quantile indexing, boundary conditions, return types), (2) Check for type annotation inconsistencies (e.g., `int` returned in `dict[str, float]`), (3) Test edge cases (empty sets, zero-duration phases, n=2 quantiles), (4) Verify docstring completeness against implementation. Route minor findings to focused GitHub issues rather than requiring a fix round if core logic is sound.
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