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Render and validate an MP4 from an existing `video-builder` spec JSON using the bundled Python pipeline. Use when a spec already exists and the job is actual rendering, not story or spec design.
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Based on SOC occupation classification
| name | video-render-run |
| description | Render and validate an MP4 from an existing `video-builder` spec JSON using the bundled Python pipeline. Use when a spec already exists and the job is actual rendering, not story or spec design. |
Take an existing spec, run the renderer deterministically, and validate the resulting MP4 instead of mixing render execution with preproduction planning.
ffmpeg plus ffprobe availability before running the pipeline./Users/nick/.codex/skills/video-builder/scripts/render_video.py using python3 /Users/nick/.codex/skills/video-builder/scripts/render_video.py --spec /absolute/path/to/spec.json --out /absolute/path/to/out.mp4.ffprobe or equivalent playback checks. Confirm duration, pacing, narration alignment, and whether the render is ready or needs one targeted fix.Next skill options (only if needed): $video-spec-builder — repair or rebuild the spec when the current spec is incomplete or wrong.Audit and improve a public GitHub repository's discoverability and any linked canonical site without confusing repository presentation with website SEO. Use for repository SEO, GitHub discoverability, topics/homepage/README optimization, GitHub Pages SEO, AEO, or agent-friendly public documentation.
Create, edit, analyze, or format spreadsheets (`.xlsx`, `.csv`, `.tsv`) with `openpyxl` and `pandas`, especially when formulas, references, and formatting must survive the change. Use when spreadsheet integrity matters more than ad hoc CSV hacking.
Analyze and reshape spreadsheet, CSV, or TSV data with `pandas` and stable output files. Use when the job is aggregation, filtering, joins, metrics, or tabular transformations rather than workbook-style editing.
Create or edit `.xlsx` workbooks while preserving formulas, references, formatting, and layout. Use when workbook integrity matters more than raw data analysis.
Produce a fast plain-text transcript with the bundled CLI using the light transcription model. Use when speaker diarization is not needed.
Produce a speaker-aware transcript with diarization and optional known-speaker hints using the bundled CLI. Use when the user explicitly needs speaker labels.