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Quality assurance and testing protocols for statistical software
Passed 9 of 20 test cases with the skill, and 8 of 20 without it.
1 fixed9 passed either way12 still failing0 broke
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Quality assurance patterns and testing strategies for statistical R packages
Use this skill when working on: R package testing, numerical accuracy validation, reference implementation comparison, edge case identification, statistical correctness verification, or software quality assurance for methodology packages.
Unlike typical software where "correct output" is clear, statistical software must:
▲
/│\
/ │ \
/ │ \
/Manual│\ <- Human review of outputs
/ Tests │ \
/─────────────\
/ Integration \ <- Cross-function workflows
/ Tests \
/───────────────────\
/ Statistical Tests \ <- Correctness verification
/ \
/─────────────────────────\
/ Reference Tests \ <- Match known results
/ \
/───────────────────────────────\
/ Unit Tests \ <- Individual functions
test cases passed, vs 8 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.