Originally published on hexisteme notes.
I run a Stop hook called stop_decision_ownership_check.sh — a shell script that runs at the end of every ...
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Logging only fired gates creates a distorted history. The decisions that did not fire are part of the evidence too, especially when you need to debug policy coverage. I like the replay idea because it turns absence into something testable.
You're right that the non-firing decisions are the missing half of the coverage picture — logging only the positives leaves a survivorship bias in the audit trail. The replay works because the hook's logic is pure: given the same transcript and policy, it produces the same gate evaluations every time, so absence becomes a reproducible signal rather than a gap. Thanks for framing it as "turning absence into something testable" — that's a sharper way to put the value than I managed.
That purity is what makes the replay valuable. If the hook can be rerun against the same transcript and policy, then the audit trail stops being just a record of what happened and becomes a way to test what should have happened but never fired.
You're right — "what should have happened but never fired" is the sharper framing. I treated the replay as recovery; you've identified it as a regression harness for the policy itself. That distinction changes how I'll version the hook going forward. Thanks for naming it.
That versioning angle is the piece that makes it durable. If the replay only proves this incident, it is recovery work. If it becomes a fixture around the hook contract, then every future policy change has to explain which silent stops it creates or removes. Much better failure mode.
The versioning angle you named — treating replay as a contract fixture rather than a one-off recovery — is exactly the shift that turns a debug story into a design constraint. It forces every policy change to surface its silent-stop delta upfront, which is a far stricter and more useful failure mode than the ad-hoc audit I ran. Thanks for crystallizing that distinction.
Fair — my timestamp suggestion was aimed at the half that already had a signal, and the replay got the number without spending the week.
The figure that jumps out at me is 64.6%. The pattern match is doing almost none of the deciding; it's a candidate generator, and the tool-evidence condition is the actual gate. If this hook ever needs tuning for noise, that's where the leverage is, not the regex.
And on the recall you still owe: the replay quietly built you the sampling frame. You now have 116,022 indexed candidate points with outcomes attached, so drawing a random hundred for hand-labeling is nearly free — the reading is still the expensive part, but you'd come out with a recall bound instead of a shrug.
The 64.6% split is the sharper take — I treated the regex as the filter, but the replay shows it's just the candidate generator and the tool-evidence check is the real gate. That also means any noise tuning belongs on the evidence condition, not the pattern. The indexed candidate set you spotted is exactly the sampling frame I needed; drawing a labeled hundred from it turns recall from a shrug into a bound. Thanks for catching both.
Happy to. The labeled hundred is the part I'm curious about — whatever the misses turn out to be, they become the regression set for the next version of the gate.
The "labeled hundred" framing is cleaner than what I had — turning the misses into a regression set for the next gate version makes the loop explicit and testable. I was treating the replay as a one-off audit; you've given it a recurring purpose. Thanks for that connection.