How do I take detailed md file instructions and an input youtube video then generate detailed high quality remotion text/image animations using the prompt and instructions (if prompts don't exists create them) then place them exactly during in the right spots (when a presenter starts talking about it, to when the presenter finishes it)

the agent that answers this

YouTube Remotion Overlay Animator. Take detailed md file instructions and an input youtube video then generate detailed high quality remotion text/image animations using the prompt and instructions (if prompts don't exists create them) then place them exactly during in the right spots (when a presenter starts talking about it, to when the presenter finishes it)

• Step 1tool

  Read and parse the provided Markdown instruction file into a structured creative brief: requested overlays, desired text/image beats, any explicit prompts, style constraints, forbidden placements, priority moments, aspect ratio, and deliverable requirements. If the Markdown omits image or animation prompts, create strong prompts from the surrounding topic and mark them as generated.

  Your model fills this step
• Step 2tool

  Preflight: immediately before the first dependency-using step, make sure the remaining work has what it needs, so the run never fails halfway on a missing tool or an unconfigured account. Command-line tools (free, auto-install only if missing; idempotent, later runs find them present): Remotion - npx remotion versions (auto-fetches), or `npm i remotion` in the project; yt-dlp - `yt-dlp --version` || install (`brew install yt-dlp` / `pipx install yt-dlp`).

  Your model fills this step
• Step 3tool

  Acquire the source video and transcript/headline metadata from the YouTube URL using headless-safe methods: public transcript if available, yt-dlp or equivalent for a local source copy when permitted, otherwise ask the user for a local source video/transcript file. Do not use an interactive browser for this step.

  Your model fills this step

  • No integration? Ask the user to provide a local MP4 if the YouTube video cannot be downloaded headlessly.
  • No integration? Ask the user to provide a transcript or captions file if no transcript can be fetched.
• Step 4tool

  Normalize the transcript into a precise timeline with speaker/presenter segments, sentence boundaries, topic spans, and start/end timestamps. Identify when the presenter starts and finishes each concept that maps to the Markdown instructions.

  Your model fills this step
• Step 5

  Map the creative brief onto the aligned transcript. For each requested or inferred animation, choose the exact start time when the presenter begins discussing the relevant idea and the exact end time when they finish it. Produce a beat sheet with overlay text, image prompt or selected asset, animation direction, transition style, screen position, safe-area constraints, z-index rules, and reason for each timestamp.

  Your model fills this step
• Step 6decision

  Review the timestamped animation beat sheet before code generation. Hold for user approval if the mapping is ambiguous, if the Markdown instructions conflict with the transcript, or if any overlay would cover the presenter or important on-screen content.

  Decision step
• Step 7tool

  Generate or collect all image assets required by the approved beat sheet. Use explicit prompts from the Markdown when present; otherwise use the generated prompts from the brief. Save assets into the Remotion project public/ directory with deterministic names and write an asset manifest.

  Your model fills this step

  • No integration? If image generation is unavailable, create placeholder-safe asset slots and ask the user for image files or API credentials.
  • No integration? If the Markdown already provides local image paths, copy those images into the Remotion public/ directory instead of regenerating them.
• Step 8tool

  Create or update the Remotion project. Add a composition that plays the source video and overlays the approved text/image animations using frame-accurate timing. Use reusable React components for text callouts, image cards, masks, transitions, easing, entrance/exit timing, and safe-area placement. Reference assets from public/ with staticFile().

  Your model fills this step
• Step 9tool

  Run local validation before rendering: typecheck/lint where available, inspect the Remotion composition duration and FPS against the source video, verify every overlay falls within the video duration, ensure assets exist in public/, and produce a frame/timestamp QA report for all overlay boundaries.

  Your model fills this step
• Step 10tool

  Render the final MP4 locally with the Remotion CLI to the configured output path. Record the render command, output path, duration, file size, and any warnings. If render fails, fix the smallest code or asset issue and retry once before asking for input.

  Your model fills this step
• Step 11tool

  Prepare the final delivery note: include the final MP4 path, Remotion project path, asset manifest path, QA report path, and a concise table of animation beats with timestamps, prompt provenance, and transcript evidence.

  Your model fills this step
• Step 12decision

  Quality gate: adversarially critique the deliverable produced by the prior steps, from three lenses (a domain expert, a hard skeptic, and the end user). Check it actually accomplishes the job, is accurate and on-brand, and is genuinely high quality. Fix clear problems in place; if something should block or materially change it, say so before it reaches the user.

  Decision step
• Step 13decision

  Voice Pass: before showing any user-facing text, rewrite the final draft so it sounds natural, platform-native, and non-generic. Preserve the meaning, facts, links, citations, decisions, and concrete details. Remove obvious AI tells: generic praise, inflated significance, rule-of-three padding, em/en dash overuse, chatbot artifacts, fake-candid openers, promotional filler, vague hedging, and formulaic conclusions. Match any provided voice samples, agent memory, prior feedback, and platform norms (Reddit/HN/social/email); if none exist, use a concise natural voice. Do not add unsupported claims. For factual, technical, legal, or reference outputs, keep the voice plain and neutral rather than adding personality.

  Decision step

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