* fix(hdr): filter zero-opacity elements and support overflow:hidden clip rects in HDR compositor
Two bugs in the HDR render pipeline:
1. Child data-start elements inside a parent with opacity:0 were still
composited as independent layers, painting over content in later scenes.
Fix: filter elements with effective opacity 0 before groupIntoLayers().
2. CSS overflow:hidden on ancestor elements was ignored for HDR video layers,
causing videos inside clipped containers (e.g. split-screen halves) to
render full-frame. Fix: add clipRect to ElementStackingInfo, compute it
from ancestor overflow:hidden in queryElementStacking(), and crop the
source buffer to clip bounds before blitting in blitHdrVideoLayer().
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(hdr): move opacity filter into blit loop to preserve hide-list correctness
The previous approach filtered zero-opacity elements before groupIntoLayers(),
which broke the DOM screenshot hide-list — invisible video elements' <img>
replacements weren't properly hidden from sibling layer screenshots, causing
the vignelli-stacking regression.
Fix: keep all elements in groupIntoLayers() for correct hide-list generation.
Skip zero-opacity HDR elements only during the actual blit step with an early
`continue` in the compositing loop.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(hdr): route identity-matrix HDR elements through region blit for clip rect support
parseTransformMatrix returns a valid matrix even for untransformed HDR
elements (Chrome reports matrix(1,0,0,1,0,0)). This made the affine blit
path always run, bypassing the region blit path which is the only one that
applies clip rects from overflow:hidden ancestors.
Fix: detect identity matrices and route them through the region path so
the cropRgb48le clip logic is reachable for split-screen layouts.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(hdr): handle translation-only matrices for clip rect support
The previous isIdentity check only caught matrix(1,0,0,1,0,0). Elements
with layout translation (e.g. right-half split at left:960px reporting
matrix(1,0,0,1,960,0)) still routed through the affine path where clip
rects are not applied.
Fix: check for translation-only matrices (scale=1, rotation=0, any tx/ty)
and route those through the region blit path. el.x/el.y from
getBoundingClientRect already include the translation, so the region path
handles positioning correctly.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(render): auto-detect HDR from media probes, add --sdr flag
Replace the --hdr opt-in model with automatic detection. When no flags
are passed, the renderer probes all video/image sources and enables HDR
output if any HDR color space is detected. Existing --hdr flag becomes
a force override. New --sdr flag forces SDR output.
Behavior matrix:
(no flags) + HDR content → HDR output
(no flags) + SDR content → SDR output
--hdr → force HDR (defaults to HLG if no HDR sources)
--sdr → force SDR (skips probing)
--hdr --sdr → error
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Revert "feat(render): auto-detect HDR from media probes, add --sdr flag"
This reverts commit 69fb52196f.
* chore(hdr): simplify review fixes — remove redundant guard, add image clip warning
- Remove redundant viewportMatrix.length >= 6 check (parseTransformMatrix
always returns 6-element array or null)
- Add clip rect warning log to blitHdrImageLayer for parity with video path
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(render): auto-detect HDR from media probes, add --sdr flag
Replace the --hdr opt-in model with automatic detection. When no flags
are passed, the renderer probes all video/image sources and enables HDR
output if any HDR color space is detected. Existing --hdr flag becomes
a force override. New --sdr flag forces SDR output.
Behavior matrix:
(no flags) + HDR content → HDR output
(no flags) + SDR content → SDR output
--hdr → force HDR (defaults to HLG if no HDR sources)
--sdr → force SDR (skips probing)
--hdr --sdr → error
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: align HDR auto-detect docs and tests
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fast reference for non-technical video editors and creatives — covers the
fast loop, terminal shortcuts, Studio keyboard shortcuts, CLI commands,
timing attributes, render presets, publish/share, and quick fixes.
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.
Adds 11 progressively-disclosed reference files that the skill loads on
demand during translation. Total ~1500 LOC, every file under 200 lines
(skill-creator's progressive-disclosure budget).
api-map.md the comprehensive Remotion -> HF translation table
(the index; loaded at start of translation)
timing.md interpolate, spring (validated configs), easing,
count-up, stagger
sequencing.md Sequence, Series, Loop, Freeze, AbsoluteFill,
Composition root
media.md Audio, Video, Img, IFrame, OffthreadVideo,
staticFile, asset paths
transitions.md @remotion/transitions presentations -> manual GSAP
crossfades or HF shader-transitions
lottie.md @remotion/lottie -> HF lottie adapter (incl. AE
feature limitations note)
fonts.md Google Fonts loading, local @font-face, system
fallback noise floor
parameters.md Zod schemas, defaultProps, sync vs async
calculateMetadata
escape-hatch.md when to bow out + the runtime interop pattern
from PR #214
limitations.md known caveat patterns (volume ramps, Loop with
state, custom presentations, code-split components)
eval.md how to run the validation harness, threshold rule
of thumb, what the noise floor looks like
The references are evidence-driven rather than speculative: every spring
config, easing curve, and SSIM threshold is documented from the
validated T1/T2/T3 calibration runs (mean 0.974 / 0.985 / 0.953). The
escape-hatch boundaries match the lint blockers in PR 2 and the T4
fixtures in PR 5.
Replaces the placeholder .gitkeep from PR 1.
Adds the escape-hatch tier — lint-only fixtures that test the skill's
ability to refuse translation cleanly when it sees patterns that don't map
to HF's seek-driven model.
Cases (8 total):
01-use-state.tsx blocker: r2hf/use-state
02-use-effect-deps.tsx blocker: r2hf/use-effect-deps (multi-line body
with internal commas — regression target for
the regex bug fix in PR 2)
03-async-metadata.tsx blocker: r2hf/async-metadata
04-third-party-react.tsx blocker: r2hf/third-party-react-ui (@mui/material)
05-lambda-config.tsx blocker: r2hf/lambda-import
06-warnings-only.tsx warnings: delayRender / useCallback / useMemo
(no blockers — translates after dropping wrappers)
07-custom-hook.tsx warning: r2hf/custom-hook (pure useFadeIn)
08-mixed.tsx multiple blockers + warnings (aggregate test)
Each case documents:
- The Remotion pattern it demonstrates
- Why it's a blocker / warning / info
- What the skill should do (refuse / drop-and-translate / translate-as-is)
Validation harness (validate.sh):
Runs lint_source.py against each case, asserts:
- Each expected blocker rule fires with severity="blocker"
- Each expected warning rule fires with severity="warning"
- lint_source.py exit code is 1 when blockers expected, 0 otherwise
T4 has no renders to diff. The skill is graded on lint correctness — that's
the gate that decides whether to translate or recommend the runtime interop
pattern from PR #214.
Result: 8/8 cases pass.
Adds the data-driven tier — a purpose-built fixture (option 2 from the
stack discussion, not a port of PR #214's examples/remotion-full/) that
exercises the realistic shape of a production Remotion composition
without using the runtime adapter.
Stargazed.tsx (10s @ 30fps, 1280x720):
Sequence 0-3s TitleScene (title + subtitle)
Sequence 3-7s StatsScene (3 reused StatCards staggered 12 frames apart)
Sequence 7-10s OutroScene (UnderlinedText with scaleX-from-left underline)
Composition shape exercises:
- <Composition schema={z.object({...})} defaultProps={...} />
- nested array prop (stats[]) materialized as repeated HTML
- custom React subcomponents (StatCard, AnimatedNumber, UnderlinedText)
reused with different props
- per-instance delay via prop (delayInFrames -> GSAP timeline offset)
- frame-driven count-up (AnimatedNumber, manual cubic ease-out)
- two different spring configs in the same composition
(damping:12 -> back.out(1.4), damping:14 -> back.out(1.2))
- useCurrentFrame, useVideoConfig
Translation choices documented in README.md and expected.json:
- Zod props -> data-* on root #stage div
- Custom subcomponents inline as repeated HTML using prop interface
as the template
- AnimatedNumber's frame-driven count-up -> GSAP onUpdate tween on a
{ v: 0 } counter object, ease power3.out
- Two different spring configs -> two different back.out overshoots
(1.4 vs 1.2 approximates the damping difference)
- delayInFrames={i * 12} -> GSAP offset (i * 0.4)s
Validated end-to-end: rendered Remotion baseline + HF translation, ran
scripts/render_diff.sh.
measured mean SSIM 0.953
measured min SSIM 0.927
measured p05 SSIM 0.938
threshold 0.90 (~0.04 below p05)
The wider gap vs T1/T2 reflects T3's bigger approximation budget
(2 spring instances + count-up timing + font fallback on multiple text
sizes). Mean SSIM below 0.90 = structural mismatch (wrong durations,
wrong stagger, missing prop wiring), not approximation drift.
Same Remotion config as PR 3: setVideoImageFormat("png") +
setColorSpace("bt709") to match HF's yuv420p output.
Lint: 9 files scanned, 0 blockers / 0 warnings / 0 infos.
oxlint, oxfmt, typecheck all pass.
The fixture is not yet wired into CI; render + diff is documented in
README.md and runs by hand via the harness from PR 2. PR 7's orchestrator
will wire all four tiers into a CI eval run.
Adds the first two test fixtures the skill is graded against. Each fixture
ships:
- remotion-src/ full Remotion project (package.json, src/, remotion.config.ts, tsconfig.json)
- hf-src/ hand-translated HyperFrames composition (index.html)
- expected.json tier metadata + SSIM threshold + translation notes + measured validation
- README.md human walk-through of the translation choices
- setup.sh (T2 only) generates binary assets (PNG, WAV) via ffmpeg
T1 — title-card-fade
- 3 s @ 30 fps, 1280x720
- Single AbsoluteFill, single useCurrentFrame interpolate
with multi-segment input [0,15,75,90] -> [0,1,1,0]
- Validated mean SSIM 0.974, threshold 0.95
(~0.025 gap from font-fallback divergence between Remotion's bundled
Chromium and HF's chrome-headless-shell)
T2 — title-image-outro
- 6 s @ 30 fps, 1280x720, three Sequences (TitleScene, ImageScene, OutroScene)
- Exercises spring, interpolate, Audio, Img, staticFile
- Spring -> GSAP back.out(1.4) translation
- Validated mean SSIM 0.985, threshold 0.95
(translation came out cleaner than predicted; spring->back.out drift was
smaller than the ~0.05 budget I'd expected)
- setup.sh generates a 200x200 blue PNG and a 6 s silent WAV via ffmpeg
so binaries stay out of the repo
Calibration done end-to-end: rendered Remotion baseline + HF translation,
ran scripts/render_diff.sh, set thresholds ~0.02 below measured p05.
Critical Remotion config: setVideoImageFormat("png") + setColorSpace("bt709").
The default JPEG output writes yuvj420p (full-range) which costs ~0.05 SSIM
vs HF's yuv420p (limited-range). Both fixtures' remotion.config.ts encode
this so render_diff.sh measures translation fidelity, not encoder differences.
Both fixtures lint clean (0 blockers via scripts/lint_source.py).
T2 staticFile() references correctly flagged as info-level findings.
The fixtures are not yet wired into CI — that comes with PR 7's orchestrator.
For now, render and eval are documented in each README and run by hand.
Adds the deterministic eval primitives the skill calls into:
scripts/render_diff.sh SSIM diff between two MP4s, JSON summary, configurable threshold
scripts/frame_strip.sh side-by-side comparison strip for visual debugging
scripts/lint_source.py pre-translation lint over Remotion source — blocks/warnings/infos
The harness is decoupled from the render pipeline: it accepts paths to
already-rendered MP4s. The skill orchestrator (PR 7) drives both renders
and feeds the outputs in. This keeps the harness usable in CI, in
sandboxes, and on any machine that has ffmpeg without needing the full
Remotion + HyperFrames toolchain.
Lint catches the patterns from the skill's out-of-scope list:
- useState / useReducer (state-machine driven animation)
- useEffect with deps (side effects)
- async calculateMetadata (Promise-returning composition metadata)
- @remotion/lambda imports
- third-party React UI libraries (MUI, Chakra, Mantine, antd, shadcn, Radix, NextUI)
- delayRender / useCallback / useMemo (warnings)
- staticFile / interpolateColors (info — translatable but flagged)
Smoke test (scripts/tests/smoke.sh) exercises all three scripts against
synthetic inputs: identical ffmpeg testsrc videos pass at threshold 0.99,
different ffmpeg testsrc videos fail at 0.99, frame_strip produces a
strip.png, lint produces 0 blockers on a clean fixture and >=3 blockers
on a fixture that uses useState + useEffect + MUI + async metadata.
Validated locally: smoke.sh exits 0.
Per Miguel's review: the previous fixture had no body / root background, so it
passed against both the buggy and fixed code. The fix this PR makes (the
initTransparentBackground stylesheet injection in initializeSession) only
matters when a composition paints over the CDP default-background-color
override — exactly what we tell users not to do, but exactly what a
regression test must do.
Reproduced locally:
- base SHA (2935be6): pixel (10,10) decodes as rgba [16,16,16,255]
(opaque #111 body bg leaks through the pre-navigation override that
Chrome resets on goto)
- this head: pixel (10,10) decodes as rgba [0,0,0,0]
(initTransparentBackground injects [data-composition-id]{background:transparent !important}
AFTER navigation, force-overriding the body bg)
The pixel-level assertions in transparency-test.ts are unchanged — they
already require alpha=0 at (10,10). With the body bg painted, that
assertion now fails on any code path that doesn't actually preserve alpha
end-to-end.
Extends RenderConfig.format with "png-sequence" and patches two correctness
gaps so the existing "webm" / "mov" values actually preserve the alpha
channel end-to-end.
Engine fixes:
- screenshotService.pageScreenshotCapture: drop optimizeForSpeed for PNG
captures. The fast path uses an alpha-unaware codec that crushes real
alpha values; kept for opaque jpeg captures where it is harmless.
- frameCapture: replace the inline setDefaultBackgroundColorOverride
block (which fired pre-navigation and was reset by page.goto) with a
proper initTransparentBackground() call inside initializeSession,
after the window.__hf readiness poll. This also injects the
html/body/[data-composition-id]{background:transparent !important}
stylesheet so compositions with custom body / #root backgrounds do not
defeat the override. Wired into both screenshot-mode and beginframe-mode
branches.
Producer:
- RenderConfig.format extended to "mp4" | "webm" | "mov" | "png-sequence"
with full JSDoc.
- Streaming encode is bypassed for png-sequence (frames go straight to
disk). FORMAT_EXT extended.
- New Stage-5 png-sequence branch: mkdir outputPath, copy captured PNGs as
frame_NNNNNN.png, copy audio.aac sidecar when audio is present.
- Stage-6 mux/faststart and the debug copy are wrapped in !isPngSequence.
- README.md: new "Transparent Video Output" section.
Tests:
- New fixture tests/transparency-regression/ tagged "transparency".
- New tsx script src/transparency-test.ts asserts pixel-level alpha for
webm + png-sequence outputs. Wired as "test:transparency".
- Default "test" / "test:update" scripts pass --exclude-tags transparency
so the golden-MP4 harness ignores the new fixture.
Verified locally on macOS arm64: typecheck clean across engine + producer,
producer renderOrchestrator vitest 10/10, transparency-test passes for
both webm and png-sequence with end-to-end pixel assertions.
Adds the directory + SKILL.md frontmatter for a new skill that translates
Remotion (React) compositions to HyperFrames (HTML+GSAP). This is the
foundation PR; subsequent PRs in the stack add the eval harness, test
corpus, translation references, and finally the SKILL.md body.
The frontmatter description enumerates trigger phrases and explicit
out-of-scope cases (useState/useEffect, async metadata, @remotion/lambda)
so the skill bows out cleanly when a Remotion composition isn't a clean
translation target — those should use the runtime interop pattern from
PR #214 instead.
Validated with skill-creator's package_skill.py.
Miguel Ángel