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Turn storyboard OCR into an evidence-backed product summary with likely views, capabilities, and open questions.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | storyboard-understand |
| description | Turn storyboard OCR into an evidence-backed product summary with likely views, capabilities, and open questions. |
Use this after storyboard-ocr when an agent needs a first-pass
structured understanding rather than raw OCR lines.
This skill owns:
Provide either storyboardDir or ocrPath:
{
"storyboardDir": "./output/storyboard"
}
If storyboard.transitions.json exists in the storyboard directory, the skill
can incorporate transition-derived flow segments.
The skill writes storyboard.summary.json. The summary includes app-name
guesses, likely views, OCR quality notes, sampling notes, interaction segments,
likely flow, capability claims with frame/line evidence, text-dominance signals,
and open questions.
Run after storyboard-ocr. For stronger flow summaries, run
storyboard-transitions first. Use this output to orient a reviewer, then
verify important claims against storyboard.ocr.json and the source frame
images.
Capability claims are pattern-based and must cite OCR evidence. App names and
view names are guesses, especially when OCR is sparse or narration text
dominates. textDominance.likelyNarrationDominated means the sampled text may
describe the product rather than come from the UI itself.
Do not present guesses as facts. Abstain from final product claims when
ocrQuality.lowSignal is true, most frames are weak/rejected, or the relevant
claim appears only in narration-like text. Keep open questions visible rather
than filling gaps from domain assumptions.
Missing OCR input fails the run. Missing transitions does not fail the run, but flow and interaction segments may be less specific. Poor sampling or OCR noise can produce generic app/view labels; inspect evidence before reusing them.
Repo-side runner:
node --import tsx scripts/harness/storyboard-understand.ts
Installed-pack runner:
node ./.video-evaluator/agent/run-tool.mjs storyboard-understand
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.