Analyze production code in any supported language by reasoning about hypothetical mutations, then confirming them against the real test suite. This reveals blind spots where tests pass but would continue to pass even if the code were broken.
Language-specific guidance
Call the
test-analysis-extensions
skill to discover available extension files, then read the file matching the target codebase (e.g.,
extensions/dotnet.md
,
extensions/python.md
,
extensions/typescript.md
). The extension file helps you find test files, recognize framework-specific assertion APIs, and identify language-specific null/None/nil patterns and error-handling idioms that map to the mutation catalog below.
Why Pseudo-Mutation Matters
Code coverage tells you what code ran during tests. It does
not
tell you whether tests would fail if that code were wrong. A method can have 100% line coverage but zero tests that would catch a sign flip, an off-by-one error, or a removed null check.
Pseudo-mutation analysis asks:
"If I changed this line, would any test fail?"
When the answer is "no," you've found a test gap.
Coverage Metric
What It Measures
What It Misses
Line coverage
Which lines executed
Whether assertions verify those lines' behavior
Branch coverage
Which branches taken
Whether both branches produce different asserted outcomes
Mutation score
Whether tests detect code changes
Nothing — this is the gold standard
This skill uses
static pseudo-mutation
to find mutation candidates at the speed of code review, then
confirms each reported survivor by actually applying it and re-running the covering tests
(Step 4b). Reasoning finds the candidates; execution decides the verdict.
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May 11, 2026
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