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James b7769b2364 feat(skills): remotion-to-hyperframes SKILL.md + orchestrator (7/7)
The leaf PR. Replaces the placeholder SKILL.md from PR 1 with the real
5-step workflow that loads the per-topic references on demand
(skill-creator's progressive-disclosure pattern), and adds a top-level
orchestrator that runs every tier and reports a pass/fail summary.

SKILL.md changes:
  - Frontmatter unchanged from PR 1 (already covers the trigger phrases
    and out-of-scope cases)
  - Body rewritten as a 5-step workflow:
      1. Lint (load escape-hatch.md if blockers)
      2. Plan (load api-map.md, then per-topic references on demand)
      3. Generate (HF index.html with paused GSAP timeline)
      4. Validate (render_diff.sh against per-tier threshold)
      5. Document gaps (TRANSLATION_NOTES.md if needed)
  - Includes a "Source contains -> Load reference" table so the agent
    only loads the references the source actually needs
  - Documents the validated baseline numbers (T1 0.974, T2 0.985,
    T3 0.953, T4 8/8) so reviewers can reproduce
  - Calls out the critical Remotion encoder config (PNG + BT.709) that
    avoids the ~0.05 SSIM hit from yuvj420p vs yuv420p

Orchestrator (assets/test-corpus/run.sh):
  - Iterates tier-1-* through tier-4-* directories
  - T1-T3: setup -> lint -> npm install (lazy) -> render Remotion ->
           render HF -> SSIM diff at the fixture's expected threshold ->
           generate strip on failure
  - T4: validate.sh (lint-only)
  - Emits run-report.json with per-tier pass/fail and aggregate counts
  - Accepts a single-tier argument for fast iteration: ./run.sh tier-1-title-card

Validated end-to-end on a clean checkout:
    ▶ tier-1-title-card → mean SSIM 0.9739 (≥ 0.95) ✓
    ▶ tier-2-multi-scene → mean SSIM 0.985292 (≥ 0.95) ✓
    ▶ tier-3-data-driven → mean SSIM 0.952941 (≥ 0.9) ✓
    ▶ tier-4-escape-hatch → 8/8 cases ✓
    passed 4/4, failed 0, skipped 0

Closes the 7-PR stack: scaffold, eval harness, 4 tiers of corpus,
references, and now the SKILL.md body that ties everything together.
2026-04-28 00:00:10 +00:00

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name, description
name description
remotion-to-hyperframes Translate a Remotion (React-based) video composition into a HyperFrames HTML composition. Use when (1) the user provides Remotion source (`.tsx` files using `useCurrentFrame`, `Sequence`, `AbsoluteFill`, `interpolate`, `spring`, `staticFile`, etc.) and asks to port, convert, or migrate it to HyperFrames; (2) the user pastes a Remotion entry point (`Root.tsx`, `Composition`) and wants HTML; (3) the user links a Remotion repo and asks for the HyperFrames equivalent; (4) the user says "port my Remotion project", "translate this Remotion code", "rewrite as HTML", or "I have a Remotion comp, make it HyperFrames". Skill detects unsupported patterns (useState, useEffect with side effects, async calculateMetadata, third-party React component libraries, `@remotion/lambda` features) and recommends the runtime interop escape hatch instead of attempting a lossy translation.

Remotion to HyperFrames

Overview

Translate Remotion (React-based) video compositions into HyperFrames (HTML + GSAP) compositions. Most Remotion idioms have direct HyperFrames equivalents — the translation is mechanical for ~80% of typical compositions. This skill encodes the mapping and guards against the lossy 20% by refusing to translate patterns that don't fit HF's seek-driven model and recommending the runtime interop pattern from PR #214 instead.

The skill ships with a tiered test corpus (T1T4, 4 fixtures total) that grades translations against measured SSIM thresholds. Don't translate without running the eval — a translation that "looks right" but renders 0.05 SSIM lower than the validated baseline is silently wrong.

Workflow

Step 1: Lint the source

Run scripts/lint_source.py over the Remotion source directory. The lint detects patterns that can't translate cleanly:

  • Blockers (refuse + recommend interop): useState, useReducer, useEffect/useLayoutEffect with non-empty deps, async calculateMetadata, third-party React UI libraries (MUI, Chakra, Mantine, antd, shadcn, Radix, NextUI).
  • Warnings (translate after dropping the construct): @remotion/lambda config, delayRender, useCallback, useMemo, custom hooks.
  • Info (translate with note): staticFile, interpolateColors.

If any blocker fires, stop. Read references/escape-hatch.md and surface the recommendation message. Warnings don't stop translation — drop the offending construct in step 3 and note the gap in TRANSLATION_NOTES.md. @remotion/lambda config is the canonical warning case: the skill drops the import + renderMediaOnLambda(...) calls but translates the rest of the composition.

Step 2: Plan the translation

Read references/api-map.md — the index of every Remotion API and its HF equivalent or per-topic reference. Identify which topic references you'll need based on what the source uses:

Source contains Load reference
Composition, defaultProps, schema, calculateMetadata parameters.md
Sequence, Series, Loop, AbsoluteFill, Freeze sequencing.md
useCurrentFrame, interpolate, spring, Easing, interpolateColors timing.md
Audio, Video, Img, IFrame, staticFile, delayRender media.md
TransitionSeries, @remotion/transitions transitions.md
@remotion/lottie lottie.md
@remotion/google-fonts/<Family>, Font.loadFont, @font-face fonts.md

Don't load all of them — load only what the specific source needs.

Step 3: Generate the HF composition

Emit index.html with:

  • Root <div id="stage"> carrying the composition's data-composition-id, data-start="0", data-duration (in seconds), data-fps, data-width, data-height, plus one data-* per scalar prop.
  • A flat list of scene divs with data-start / data-duration / data-track-index.
  • Inline <style> for layout; CSS sets the from state of every animated property.
  • A single <script> tag at the bottom containing one paused gsap.timeline({paused: true}). Every Remotion useCurrentFrame() derivation becomes a tween on this timeline at the right offset.
  • window.__timelines["<composition-id>"] = tl; registers the timeline with HF's runtime.

Custom React subcomponents inline as repeated HTML using the prop interface as the template (see parameters.md for the per-instance data-* pattern).

Step 4: Validate

Run the eval harness — references/eval.md for the full guide. Quick path:

# Render Remotion baseline (after npm install in the fixture)
cd remotion-src && npx remotion render <CompositionId> out/baseline.mp4

# Render HF translation
cd ../hf-src && npx hyperframes render --output ../hf.mp4

# SSIM diff
../../scripts/render_diff.sh ./remotion-src/out/baseline.mp4 ./hf.mp4 ./diff

Threshold: ~0.02 below p05 of the source's complexity tier (see eval.md's validated thresholds table). If the diff fails, run scripts/frame_strip.sh to see which frames diverged, then re-read the relevant timing/sequencing/media reference.

Critical: both renders must use matching pixel format. Set Config.setVideoImageFormat("png") + Config.setColorSpace("bt709") in the Remotion source's remotion.config.ts — otherwise the diff measures encoder differences (~0.05 SSIM hit), not translation fidelity.

Step 5: Document gaps

Anything that didn't translate cleanly (volume ramps dropped, custom presentations approximated, fonts substituted) gets a TRANSLATION_NOTES.md written next to the HF output. See references/limitations.md for the format.

What this skill explicitly does NOT do

  • Translate React state machines. Compositions that drive animation via useState + useEffect are not deterministic frame-capture targets in HyperFrames' seek-driven model. Recommend the runtime interop pattern.
  • Run Remotion's render pipeline alongside HyperFrames. That's the runtime interop pattern from PR #214 — a separate solution for compositions that fail this skill's lint.

(@remotion/lambda is not a blocker — Lambda config is deployment, not animation. The skill drops it as a warning and translates the rest. See references/escape-hatch.md.)

How to grade your own translation

Run the test corpus orchestrator:

./assets/test-corpus/run.sh

It runs T1, T2, T3 (render + diff) and T4 (lint validation), prints a per-tier pass/fail table, and emits an aggregate JSON report. Use this to verify the skill is working end-to-end on a clean checkout — and as a regression check after editing any reference.

Validated baseline (as of 2026-04-27):

Tier Composition shape Mean SSIM Threshold
T1 single-element fade-in 0.974 0.95
T2 multi-scene + spring + audio + image 0.985 0.95
T3 data-driven, custom subcomponents, count-up 0.953 0.90
T4 escape-hatch (8 lint cases) 8/8 pass n/a