gating

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Build and audit deterministic verification gates — checks that block a pipeline and can be shown to go red. Use when writing a calibration gate, CI check, validation script, or pre-publication check for a numeric or empirical result; when a result is about to be published, acted on, or merged and a plausible-but-wrong value would survive review; when asking whether an existing test suite, linter rule, or check could actually fail; and when a check suite passes first try, passes suspiciously often, or was written by the same process that produced the thing it checks. Triggers on "verification loop", "calibration gate", "can this check fail", "known-bad", "negative control", "sanity check my results", "is this test actually testing anything".

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Skill Content

# gating A gate is a check that blocks. Its only job is to go red when it should. The characteristic failure is not a wrong check — a wrong check gets noticed. It is a check that **cannot fail**, which reports PASS forever and is indistinguishable from a working one from the outside. That is what makes this different from ordinary testing: the object under suspicion is the check. ## The three obligations Every gate owes these. A gate missing any of them is not yet a gate. **1. An anchor outside your own code.** Something the check compares against that your implementation did not produce: a published constant, a closed-form answer, a conservation law, a degenerate case with a known result, an independent implementation. A check that compares this run to the last run only ever tells you the code still does what it did. See `references/anchors.md`. **2. A known-bad it demonstrably rejects.** Break the subject the way it would plausibly break, run the gate, confirm red. Until you have done this you have not shown the gate works — you have shown it runs. This is the obligation people skip, because a passing gate feels like evidence. Two things about known-bads that are easy to get wrong: - **Validate it at the configuration it will run in.** A case tuned on a small or fast setting can stop being bad at full size. An "untrained" grid built from one Lloyd iteration was genuinely zero-gain at m=2/K=16 and earned a real +0.10 dB at m=8/K=65536, where one iteration reloc...

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Author
oaustegard
Repository
oaustegard/claude-skills
Created
10 months ago
Last Updated
today
Language
Python
License
MIT

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