**Overall Verdict:** Accept — The task is complete, objectively gradable, and reachable, with minor integration and validator discrepancies that should be tightened before deployment.

**Completeness Check:**
- Instruction: Complete — The source-to-target mapping objective, geometric and hierarchical constraints, null behavior, handedness requirement, and flat JSON deliverable are clear.
- Inputs: Complete — `skeletons.json` is valid UTF-8 JSON containing 27 uniquely named source bones and 26 uniquely named target bones, each with valid hierarchy references and numeric 3D geometry.
- Toolchain: Complete — Python 3 and NumPy provide a legal, headless, single-machine workflow; the benchmark adapter dependencies are evaluator infrastructure rather than solver requirements.
- Reference output: Complete — The reference package includes a 27-entry passing mapping, grade report, static reference solver, seeded generator/grader, self-check, baseline analysis, metadata, and integration documentation.
- Rubric: Complete — Exact per-source correspondence scoring and the 1.0 pass threshold are deterministic. Minor implementation variance exists: the grader accepts extra keys and treats an empty string as null, and its structural gate does not reject arbitrary string values before scoring.

**File and Task Reachability:**
The attachment set is `reachable`: both manifest files matched their declared byte counts and actual formats. `skeletons.json` and every member of `skeleton_retargeting_map.zip` were inspected, including `main.py`, `requirements.txt`, `selfcheck.py`, `README.md`, `reference_solution.py`, `_shared_eval.py`, `task_card.json`, `SUBMISSION_FORM.md`, `INTEGRATION.md`, all three example JSON files, all documentation files, and `evaluations/agent_baselines.py`. The ZIP was readable, unencrypted, CRC-valid, path-safe, and contained only regular text files and directories; no content was left unreviewed.

The task is `reachable`: the solver reads the two skeleton trees, produces the flat mapping, and the evaluator regenerates seeded ground truth and scores every source bone. The supplied input is byte-identical to the packaged seed-1 standard-mode example, and its reference mapping covers every source bone, maps two dropped bones to null, and leaves one target helper unused. Evaluator-only separation prevents the worked mapping and reference solver from becoming solver-visible shortcuts. One integration caveat remains: the adapter currently registers public training seeds with standard mode, while the documentation describes a held-out private instance and arbitrary rotation in hard mode.

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:**
None

**Recommended Changes:**
The submission provides a coherent task, valid evidence, and a strong deterministic evaluation path. No material contributor-side changes are required for reachability. Before deployment, configure an actual held-out private seed and the intended difficulty mode, remove unnecessary seed metadata from solver-visible input, and make the grader enforce exact source-key coverage, target-name membership, and JSON null without accepting empty-string aliases.