AMC Sample Dataset Calibration

The skill scopes execution to a fixed synthetic sample, explicitly excludes user videos and RTSP, and forbids fabricated results; the script uses a throwaway virtual environment and does not require credentials. Eight points are deducted because it creates projects, uploads data, and persists server-side results without pre-execution confirmation, deletion/rollback guidance, or retention disclosure; HTTP transport, server authorization boundaries, and sensitive-data handling are also under-specified.

The documentation and script are broadly consistent and include sorted uploads, polling, timeouts, terminal-error handling, and log-tail feedback. Twelve points are deducted because static files do not prove the key path runs; the script lacks a backend preflight and clear network-exception handling, dependency installation may fail, and partial uploads have no cleanup path. The static ceiling also prevents a score above 10.

Trigger phrases, the fixed dataset, prerequisites, and routing boundaries to video/RTSP skills are clear, with both Python and Swagger paths provided. Four points are deducted for no Chinese-language guidance or localized failure messages, dependence on a local AMC service and CLI environment, and no mainland-China network or remote-deployment fit discussion; “launch and test” also depends on another skill.

The package has a stable name, version, author, owner, license, metadata, prerequisites, troubleshooting, related skills, and evaluation files, with generally good layering. Five points are deducted for the missing recommended Examples section, absent explicit changelog and stable parameter contract, unclear maintenance/update path, and an empty References section in the skill card. The license metadata also conflicts at governance level with the stated NOASSERTION.

The goal is to validate the AMC workflow end to end with a fixed sample; the API order, GT metrics, and output locations are concrete, and the committed evaluation report provides limited evidence of core value. Nine points are deducted because no independently reproducible actual output or statically verifiable representative result is included, manual UI/service inspection remains necessary, and VGGT and external-service dependencies make the full outcome variable. The static ceiling limits this score to 7.

The SKILL.md, script, evaluation tasks, and benchmark report provide auditable material and distinguish expected outcomes from execution prerequisites. Seven points are deducted because there is no committed test suite or real CI execution evidence covering the key paths, and the benchmark cannot be independently reproduced from the supplied files alone. The static ceiling limits this score to 5.