prompt: retire the three shipped Clipper follow-ups to complete/ - #192
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All three merged today, so they leave draft/. Left in place they would read as unstarted high-priority backlog on the dashboard people pick work from -- the exact drift this session opened by finding phase 1 still sitting there after it had shipped. - clipper-usage-in-search-summary PyAutoFit#1478 bbceff6 - save-json-numpy-scalar-typeerror PyAutoFit#1479 b6e89cd - crashed-run-poisons-resume PyAutoFit#1480 5c9244b Each record carries what implementing it taught, including two corrections to what the prompts asserted: - The search.summary prompt claimed there was "no search-specific channel at all". False. search_summary_from_samples already read samples_info and already emitted the NaN counters; the real gap was two omissions, not a missing mechanism. Recorded because it nearly caused a rebuild of something that existed. - The poisoned-resume prompt claimed the rerun was "a zero-step no-op that reads as a clean result". That did not reproduce; a hard JSONDecodeError on every rerun did. The no-op variant presumably needs a surviving search_internal, which the crash path deletes first. Only the crash is evidenced, and the record says so. Also kept: the second unguarded JSON writer the float32 prompt never named (Samples.info_to_json, the more dangerous one, since samples_info gains a counter every time a search does); the resume-guard leg found by audit rather than by the prompt; the test trap where a run short-circuited as already-complete passes an end-to-end resume test while asserting nothing; and the toy 3-parameter Gaussian that reproduces the #128 mechanism in seconds, which is a far cheaper regression fixture than the imaging/mge cell. active.md updated to shipped state with both corrections. Four dangling references repointed at the new records. lifecycle check + index check OK; dashboard regenerated (141 -> 138 drafts). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FzF2XmKQaqZRWZfZMxvTNR
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All three follow-ups merged today, so they leave
draft/. Left in place they would read as unstarted high-priority backlog on the dashboard people pick work from — the exact drift this session opened by finding phase 1 still sitting there after it had shipped.clipper-usage-in-search-summarybbceff6save-json-numpy-scalar-typeerrorb6e89cdcrashed-run-poisons-resume5c9244bTwo corrections the records carry
Both are cases where the prompt asserted something implementing it disproved.
The
search.summaryprompt claimed there was "no search-specific channel at all". False.search_summary_from_samplesalready readsamples.samples_infoand already emitted the NaN counters and their rates, key-guarded so other searches are unaffected. The real gap was two specific omissions —n_clipped_lane_stepsnever leavingsearch_internal, andn_constrained_lane_stepsreachingsamples_infobut never being printed — not a missing mechanism. Recorded because it nearly caused a rebuild of something that already existed.The poisoned-resume prompt claimed the rerun was "a zero-step no-op that reads as a clean result". That did not reproduce. What reproduces is a hard
JSONDecodeErroron every rerun of the same search name, naming no file and offering no remedy. The no-op variant presumably needs a survivingsearch_internalwhose restoredtotal_stepsshort-circuits the loop — the crash path deletes it first. Same root cause, fix covers both, but only the crash is evidenced and the record says so.Other things kept because they were learned, not planned
Samples.info_to_json, and the more dangerous of the two, sincesamples_infogains a counter every time a search does. #1478 added two to that very dict the same day.search_internalraised instead of falling into the fresh-start branch directly beneath it..completedmarker is not found byrglobunder the test config.imaging/mgecell.Bookkeeping
active.mdupdated to shipped state with both corrections. Four dangling references repointed at the new records.lifecycle.py checkandindex --checkboth OK;dashboard.mdregenerated (141 → 138 drafts).🤖 Generated with Claude Code
https://claude.ai/code/session_01FzF2XmKQaqZRWZfZMxvTNR
Generated by Claude Code