How do I plan and build production-quality, transcript-bound Remotion editorial animations with deliberate per-beat variety, provenance-safe assets, timestamped review clips, and no final render without approval

the agent that answers this

YouTube Remotion Overlay Animator — Review First. Plan and build production-quality, transcript-bound Remotion editorial animations with deliberate per-beat variety, provenance-safe assets, timestamped review clips, and no final render without approval.

• Step 1tool

  Bind this run to a fresh required target video and fresh required Markdown animation brief, plus an optional inspiration video. Record exact URL/path and identity or hash where available; reject missing required inputs and never replace selected inputs with prior-run media, briefs, transcripts, assets, or cached selections.

  Your model fills this step

  • No integration? If either required input is absent or unresolved, stop and request that exact target video or Markdown brief.
• Step 2tool

  Parse the bound Markdown brief into a structured beat inventory: transcript claim, required factual copy, timing, design tokens, forbidden content, presenter-safe areas, evidence needs, and animation behavior. Beat IDs such as R07 are internal filename and QA metadata only and must never appear in viewer-facing text.

  Your model fills this step
• Step 3tool

  Preflight Remotion, FFmpeg, transcript, and local video tooling immediately before dependency use; install only missing free command-line dependencies idempotently.

  Your model fills this step

  • No integration? If installation is impossible, name the exact missing command and required action.
• Step 4tool

  Acquire and validate only the bound target video and its transcript or captions, using the selected local file first and permitted headless public methods for a selected URL. Prove media and transcript belong to the bound source; never substitute prior-run files.

  Your model fills this step

  • No integration? Ask for a local MP4 if the bound source cannot be acquired headlessly.
  • No integration? Ask for a transcript or captions file tied to the bound target if none can be fetched.
• Step 5tool

  Normalize the bound transcript into a precise presenter timeline with sentence boundaries, topic spans, timestamps, and factual claims, then bind every requested beat to its exact spoken segment and source frames.

  Your model fills this step
• Step 6tool

  When an optional inspiration video is bound, analyze it before treatment planning for camera language, visual hierarchy, rhythm, substrate changes, motion grammar, typography, and design vocabulary. Extract principles only and never copy it shot-for-shot; skip cleanly when none is supplied.

  Your model fills this step
• Step 7

  Before any Remotion code, create a per-beat visual-treatment plan that preserves one coherent design system while deliberately breaking monotony. Give every beat one distinct primary role and substrate chosen from presenter-led typography, animated cost build, chart, map, sourced document/evidence card, Remotion-native diagram, or clearly illustrative editorial still. Specify hierarchy, composition, presenter visibility, motion, typography, transcript rationale, and source timing. Adjacent beats may not repeat the same primitive unless a concrete editorial reason is stated; internal beat IDs never become viewer-facing copy.

  Your model fills this step
• Step 8tool

  Before asset generation, add an asset/provenance proposal to the treatment plan. Classify every proposed still or image as factual/source evidence or illustrative. Factual evidence must name a real sourced document, real sourced map, or user-provided asset. Generated images are always illustrative and must not imply a real location, statistic, document, or event. For each useful illustrative image, provide the exact concept, prompt, placement, and provenance label; do not generate it yet.

  Your model fills this step
• Step 9decision

  Hold for explicit user approval of the timestamped beat sheet, visual-treatment plan, factual sources, and each proposed image prompt before any Remotion code or image generation. Also hold for ambiguous transcript mapping, unsafe or missing evidence, conflicting instructions, presenter obstruction, unjustified adjacent repetition, or an incoherent design system. Record approval scope precisely; treatment approval never authorizes the final render.

  Decision step
• Step 10tool

  After treatment approval, collect only authorized factual assets and create deterministic Remotion-native charts, maps, diagrams, typography, and cost builds. Generate an approved illustrative image only when its exact prompt was approved and ChatGPT image generation is available in the runtime; never assume Codex itself is an image provider or switch providers automatically. If unavailable, use a suitable Remotion-native treatment or present the prompt as an asset request, never a silent generic dark-glass card. Record source or prompt, factual-versus-illustrative label, beat, and approval evidence in the asset manifest.

  Your model fills this step

  • No integration? Use user-provided assets without regenerating them.
  • No integration? If approved generation is unavailable, preserve the prompt as an asset request or use a deliberate approved Remotion-native treatment.
• Step 11tool

  Create or update the editable Remotion project using reusable frame-accurate components and only approved treatments/assets. Preserve the shared system while implementing deliberate role and substrate changes. Keep beat IDs only in filenames, composition names, manifests, and QA. Prepare the full composition but do not render the final combined video.

  Your model fills this step
• Step 12tool

  Validate before review renders: typecheck/lint; match duration and FPS to the bound target; verify transcript timing, asset availability, provenance, generation approval, presenter safety, hierarchy, and source boundaries; confirm all animation is in range, viewer text has no internal IDs, factual images are not synthetic, and adjacent primitives do not repeat without the stated reason. Produce frame, timing, presenter-safety, visual-variety, transcript-binding, and provenance QA.

  Your model fills this step
• Step 13tool

  Render each beat as a separate inexpensive review MP4, never the final combined video. Include the matching bound target segment with original presenter audio, visible timecodes, deterministic internal filenames, and a manifest mapping every clip to transcript, meaning, treatment, assets, provenance, frames, and timestamps.

  Your model fills this step
• Step 14decision

  Judge every review clip end-to-end against the bound transcript, approved plan, asset manifest, provenance, and QA. Verify wording, comprehension, voice-aligned entrances/exits, frame math, presenter safety, coherent purposeful substrate variation, no viewer-facing IDs, and clearly non-evidentiary illustrative images. Return pass or per-clip repairs; never approve from stills, manifests, or prose alone.

  Decision step
• Step 15decision

  Final-render approval gate: allow the expensive combined render only after the user explicitly approves the delivered review clips and the clip judge passes every clip. Treatment or image approval is not final-render approval. Otherwise do not render; preserve review clips, plan, asset manifest, QA, and repairs.

  Decision step
• Step 16tool

  Only after recorded final-render approval, render the combined MP4 locally with Remotion and record command, output path, duration, size, and warnings; retry once only for the smallest approved repair.

  Your model fills this step

  • No integration? Without final-render approval, skip rendering and deliver review materials and repairs.
• Step 17tool

  Prepare a delivery note listing the editable project, bound-input ledger, inspiration analysis when supplied, treatment plan, asset manifest/provenance, QA, review manifest, timestamped clips, judge verdict, transcript evidence, and final MP4 only if approved and rendered.

  Your model fills this step
• Step 18decision

  Voice Pass: make only delivery prose concise, natural, factual, and non-generic. Preserve paths, timestamps, transcript evidence, input bindings, approval scopes, provenance, warnings, and decisions; never alter animation copy or conceal a failed or pending gate.

  Decision step
• Step 19decision

  Adversarially review as editor, fact-checker, and viewer: verify files exist, inputs match current run bindings, every factual visual has a real source, every generated visual is illustrative, beats vary purposefully within one system, and reporting separates final output from review clips.

  Decision step
• Step 20decision

  Publish the delivery note only after preserving facts, paths, timestamps, transcript evidence, bindings, judge decisions, provenance, approval state, and warnings; never claim an image, source, approval, or final render that does not exist.

  Decision step

common questions

1. v4Aug 6manual

   Added fresh input binding, inspiration analysis, varied per-beat treatments, provenance rules, and approval-gated image generation.

2. v3Aug 6manual

   added a required per-beat visual-treatment planner, asset provenance, production-quality variety QA, and internal-label leakage guard

3. v2Aug 5manual

   added 4 steps; rebound step 11 (gap -> decision), step 13 (decision -> gap)

4. v1Jul 26generated

   auto-generated from "Take detailed md file instructions and an input youtube video then generate deta"

Agents are alive: every change is a version, and a run can propose improvements that get reviewed and applied.

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