**Overall Verdict:** Accept — The revised submission is complete, coherent, reachable, and has an objective offline evaluation path; no material gap remains.

**Completeness Check:**
- Instruction: Complete — `TASK.md` and `CONTRACT.md` define the recovery workflow, schemas, transaction semantics, outputs, CLI, and acceptance conditions.
- Inputs: Complete — `input_materials_final.zip` contains all eight batches, metadata, cutoff definitions, starter code, and the legacy SQLite database; manifest counts match the 4,723 parsed records.
- Toolchain: Complete — Standard-library Python and SQLite are sufficient, with no credentials, network, or paid dependencies required. Linux/Windows portability remains uncertified but is not a material reachability issue.
- Reference output: Complete — The evaluator-only package contains the reference implementation, evaluator, oracle, saved database, all 20 snapshot artifacts, metrics, logs, authorization declaration, and a successful saved grade report.
- Rubric: Complete — The 100-point rubric covers migration, evidence retention, exact outputs, replay order, transaction recovery, perturbation behavior, and artifact completeness.

**File and Task Reachability:**

The attachment classification is `reachable`, and the task classification is `reachable`. I inspected both ZIP archives, their member inventories, integrity, regular-file modes, path safety, encodings, and declared sizes. I inspected `TASK.md`, `CONTRACT.md`, `ENVIRONMENT.md`, `manifest.json`, `cutoffs.json`, all eight JSONL batches, the starter script and database, `CONTRIBUTOR_AUTHORIZATION.md`, `EVALUATOR_README.md`, all evaluator/reference scripts, the saved grade and metrics reports, the incident report, run log, reference database, and all 20 snapshot JSON files. Every JSON artifact parsed successfully; large arrays were structurally summarized and boundary-sampled rather than manually enumerated row by row. No required attachment was unreadable or left inaccessible.

The solver-visible input package is separated from the evaluator-only reference package, and no answer artifact or evaluator implementation was found in the input package. The end-to-end path is clear: a contributor uses a standard-library Python/SQLite implementation, ingests the supplied batches, exports the five snapshots, and is checked by the independent evaluator and perturbation tests. The saved reference report records 100/100 with all checks passing. The review environment lacked a Python runtime and SQLite command-line client, so I did not execute uploaded programs or independently query the databases; this is a reviewer-tooling limitation, not a submission defect.

License keys, accounts, tokens, and other credentials are not required from the contributor.  
Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.

**Special Screening:**

`synthetic_defect_fixture` — The task text and reviewer-only fixture generator affirmatively show deliberately injected conflicts, invalid revisions, orphan operations, tombstones, replays, and tie cases as the core exercise. There is no affirmative evidence that the material is AI-generated, and public-benchmark status was not established.

**Recommended Changes:**

The revision appropriately adds the contributor rights and terms authorization that was previously missing, while preserving the task, inputs, evaluator, and generated outputs. No contributor changes are required.