Autonomous Video Pipeline

Autonomous Video Pipeline

Purpose

Produce a finished short-form video package from source material with explicit quality gates. Treat this as a production workflow, not a prompt trick: plan the artifact, build deterministic manifests, create media in replaceable provider steps, render the final MP4, then verify before claiming completion.

If the requested artifact is only a browser walkthrough or screencast, prefer browser-tutorial-video. Use this skill when narration, generated scenes, motion graphics, avatar/TTS clips, or multi-stage video assembly are involved.

Workflow

1. Pin the production contract.

   • Capture topic, audience, target duration, aspect ratio, language, output directory, allowed source material, provider constraints, budget ceiling, and whether voice/avatar likeness is explicitly authorized.
   • If paid APIs, cloned voices, avatars, private accounts, or live provider UIs are required and authorization is unclear, ask before proceeding.
   • For current or factual claims, verify from primary/current sources and keep a source ledger in the project manifest.

2. Create a project manifest.

   • Use scripts/build_video_manifest.py to create the starting manifest and project folders when useful.
   • Track deliverables, sources, narration chunks, media assets, scene files, render commands, QA evidence, and residual limitations.
   • Keep the manifest current as work proceeds. It is the control surface for review, rerendering, and handoff.

3. Write the script as a production script.

   • Use the user's voice playbook if provided; otherwise write clearly and avoid impersonating a real person.
   • Separate narration from visual direction.
   • Keep factual claims traceable to the source ledger.
   • Read references/production-prompt-pattern.md when shaping a high-stakes goal prompt or stop condition.

4. Chunk narration before media generation.

   • Use scripts/chunk_script.py to split narration into provider-friendly chunks, defaulting to sub-minute estimated speech duration.
   • Keep chunk IDs stable because downstream audio, avatar clips, subtitles, and QA notes should refer to the same IDs.

5. Create a storyboard and scene manifest.

   • Read references/storyboard-scene-manifest.md.
   • Map each narration chunk to scenes, visual intent, motion graphic needs, asset dependencies, and QA timestamps before generating expensive media.
   • Use a simple JSON or Markdown scene manifest; do not rely on memory or chat-only timing notes.

6. Generate or collect media through adapters.

   • Read references/provider-adapters.md before using TTS, avatar, stock media, browser automation, or code-rendered motion graphics.
   • Prefer APIs and local tools. Use browser automation only when the user is authorized and no stable API path exists.
   • Never hardcode one provider as required. Record the chosen provider, model/settings, input chunk ID, output path, and failure notes.

7. Build motion graphics and edit.

   • Read references/motion-graphics-rules.md before creating scene code.
   • Keep the speaker/avatar visible when the brief asks for an avatar-led explainer.
   • Use scene manifests and explicit timings instead of visual guesswork.
   • Assemble with FFmpeg or an existing repo-local render pipeline.

8. Verify before completion.

   • Read references/video-qa-checklist.md.
   • Use ffprobe metadata, QA frame extraction, representative visual review, audio/subtitle timing checks, and scene-boundary checks.
   • Use scripts/extract_qa_frames.py for deterministic frame extraction.
   • Use scripts/probe_video.py to write a compact ffprobe summary.
   • Use scripts/validate_video_package.py --final before claiming upload readiness.
   • Rerender or patch until the acceptance criteria are met, or report the exact limitation that prevents completion.

Stop Condition

Do not claim that the video is finished until these are true:

• A final MP4 exists at the declared output path.
• The manifest lists all source material, generated chunks, media assets, render steps, and verification evidence.
• ffprobe metadata matches the requested format closely enough.
• validate_video_package.py --final passes for the project manifest, or each failure is explicitly waived with a reason.
• Representative QA frames were inspected for timing, layout, clipping, blank scenes, out-of-bounds elements, and visible avatar/speaker constraints.
• Any unverified factual claims, provider failures, watermarks, account limits, or human-review dependencies are stated plainly.

Output

Return the final MP4 path, manifest path, QA evidence path, key render metadata, and any residual limitations. If the work cannot safely complete, return the best partial package with a concrete next action instead of pretending it is ready to publish.