video-evaluator

Assemble a grounded review prompt from a local artifact bundle so Codex or Claude Code can inspect the right files and ask the right questions.

Check a local video against layout annotations, safe zones, OCR text boxes, and frame samples so agents can catch cropped content, caption overlap, unreadable overlays, and platform-unsafe composition before shipping.

Collect first-pass source media facts, audio silence/energy, shot estimates, representative frame status, and persistent text/caption risk evidence.

Review a rendered video for technical defects such as resolution mismatch, missing or near-silent audio, black or white frames, edge gutters, low motion, sparse caption bands, and layout issue pass-through.

Fuse video shot segments with storyboard frames, OCR, transitions, and timeline artifacts into one per-segment evidence map for grounded video review.

Extract at least one storyboard frame per video shot segment so later OCR and segment evidence fusion can cover gaps missed by global sampling.

Normalize a local video run into a consistent artifact bundle by resolving output roots, latest pointers, known reports, and the primary video path.

Extract coarse shot and scene-change segments from a local video, with optional representative frames for each part.

Compare two local video artifact bundles and surface report-status changes, artifact presence changes, and coarse video-level deltas.

Materialize the local video-evaluator skill pack into another repo so Codex or Claude Code can use the shared review skills there.

Review a local video artifact bundle, summarize report status across known artifacts, and point the agent at the first useful review surfaces.

List the shipped video-evaluator skills so an agent can discover the shared review and artifact-intake surface.

Extract storyboard frames from a local video and write a manifest that an agent can inspect as evidence, with optional hybrid sampling around likely changes.

OCR extracted storyboard frames, write a text manifest, and surface evidence-backed UI text from a local video.

Infer frame-to-frame transitions from storyboard image diffs and OCR deltas, then write an evidence-backed transition manifest.

Turn storyboard OCR into an evidence-backed product summary with likely views, capabilities, and open questions.