code-review-evidence

Verifies code review claims and test evidence against upstream sources and real data stores.

2|Updated Jul 18, 2026
One-click install
npx skills add https://github.com/Arasz/ai-badger --skill code-review-evidence-arasz
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: code-review-evidence
Source: https://github.com/Arasz/ai-badger/tree/main/features/common/skills/code-review-evidence
Command: npx skills add https://github.com/Arasz/ai-badger --skill code-review-evidence-arasz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Code reviews often accept behavior claims from wrapper comments or specs and treat green tests as proof, even when tests assert values the code constructed itself. This Skill audits whether claims and tests constitute real evidence, catching spec-vs-coverage gaps, tautological tests, dishonest skips, and fakes that cannot model real backend semantics. ## Core Features & Use Cases - Wrapped-library verification: Confirms wrapper behavior claims (dedup scope, option persistence, delete granularity, exception hierarchies) from upstream source or live probes rather than comments or specs. - Test-harness QA: Classifies every integration assertion as tautology, weakly-observable, or observable; audits fake fidelity, skip honesty, and snapshot-only coverage across a full suite. - Live-store data checks: Queries real SQLite stores read-only to validate cardinality and aggregation claims that plans assert as ground truth. - Use Case: When reviewing a PR that wraps a SQLite extension with a 154-test suite, use this Skill to discover that the share operation is a silent no-op due to global content-hash dedup and that the only share test asserts a code-constructed value. ## Quick Start Review this pull request's integration tests and wrapper code, verifying each behavior claim against the upstream library source and flagging any tautological assertions.

Frequently Asked Questions about code-review-evidence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect tautological tests in a code review?

Check whether each integration assertion compares a field of the record the method under test constructed itself. If so, the test passes while the feature does nothing; require a fresh re-query from the store or a consuming operation instead.

How to verify wrapper claims about third-party library behavior?

Fetch the upstream source at the pinned version and read the exact functions the wrapper depends on, or run a small probe against the real binaries. Never trust the wrapper's comments or the feature spec, which can both contradict actual library behavior.

Why do integration tests pass when the feature is broken?

Common causes include tests asserting code-constructed values, fakes returning data for rows the real backend would not have, skips that report PASSED when the backend is unavailable, and snapshot tests that only detect text edits rather than broken SQL.

Can this Skill review .NET and Python test suites?

Yes, the references include worked cases for .NET (Dapper affinity, BackgroundService loops, DI guard tests) and Python (Hermes plugin reviews, pytest skip honesty). The assertion taxonomy and fake-fidelity audit apply to any xunit or pytest harness.

What are the limitations of fake-based test suites?

Fakes can only prove loop shape, not backend semantics like global dedup or global delete. If the fake's schema cannot represent the real store's cardinality, entire bug classes become structurally untestable and ship green.