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Allow research task selection from sparse checkouts - #3102
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Mister-Meeseeks merged 1 commit intoOct 17, 2026
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Change and mathematical scope
The task picker currently rejects a sparse checkout when any card's first-step script, starting point or priority log is absent, even if that card is unrelated to the requested task. It now accepts an absent reference only when it is a regular file in HEAD and has a matching, stage-zero skip-worktree index blob. Sparse omissions are explicitly listed in the JSON receipt; the seed, track, methods and scope keys are unchanged.
This is a tooling repair. It validates catalogue references, not replay dependencies; users must materialize their chosen task's inputs before execution. Scientific sources, allocations, selected records and source pins are unchanged. No supplier admission or new kappa is claimed.
Task and overlap
18aacb215fc3717b06be8ceb9fca62aeca0d3e99, priority revision 6.portable-replay/chooser-sparse-catalogue-regular-head-files; changed scope from theadversarial-auditdraw, nonce6c43ccec08828911d1cc13913048461f.Validation
Passed locally on macOS arm64, Git 2.53.0, CPython 3.12.14:
All 50 tests pass without skips (16 assignment, 8 coordination, 26 onboarding). New controls cover non-cone sparse checkout, cone sparse index, unchanged full/sparse CLI receipts, ordinary deletions, unknown paths, staged replacements, staged-only entries, omitted symlink entries, directory/external alias substitutions and Git failure. An original-source comparison rejects the same sparse fixture that the changed helper accepts. Existing full-checkout behavior remains available without Git path lookup when all references are present.
The actual sparse checkout also successfully runs:
It reports 34 omitted catalogue paths and retains all three original adversarial-audit scope keys and the unchanged selected-record SHA256
bb2eaa58647b353f88d0aec2eafb9d30df1be53589231b23e12ceef3e4bad129. The assignment tests also passed on CPython 3.14.7 before the final added cone-index control; the full 50-test result above is the canonical Python 3.12 run. Hosted CI, Windows execution and the selected mathematical cold replay are not claimed.Credit and remaining work
Credit Douglas Colkitt and the repository onboarding contributors for the picker and standing procedure. Arham Tahir prepared this focused fix with substantial OpenAI Codex assistance; Apache-2.0. This is author-checked, not independent human review.
Scientific dependency hydration remains a separate task; the new receipt explicitly says the catalogue check cannot establish replay readiness. No additional research package or mathematical obligation is introduced. Workflow feedback and scope-change rationale.