source-feedback

To identify missing visuals in media production, you can analyze the asset-manifest.json file to detect zero results, low-quality matches, or shallow source availability. This diagnostic process highlights sourcing failures and assesses their narrative importance.

The best way to resolve asset sourcing gaps is to perform a diagnostic analysis on failed shots and recommend alternative strategies. Suggestions include search term modifications, template substitutions, archival sources, AI illustrations, or script revisions to maintain production continuity.

Visual asset searches fail during production due to zero results or low-quality matches in the asset-manifest.json. A diagnostic analysis determines why specific shots failed sourcing and evaluates their overall narrative importance for the project.

Yes, you can use template substitutions to fill visual gaps in short-reaction media. When a hero shot is missing, the Skill recommends broader search terms or template-based visual alternatives to resolve the gap quickly and avoid delays.

Automated asset gap detection relies strictly on the contents of the asset-manifest.json to flag sourcing failures. It cannot invent new assets; it only recommends alternative sourcing strategies like search term modifications, archival sources, or script revisions.