Windows sizes Python's stdio and text-mode file IO to the ANSI code page
(cp1252), not UTF-8. Every skill Python script relied on that default:
* analyze-beatgrid.py --print writes the glyphs cp1252 has no slot for
(delta, arrow), so the brief died with UnicodeEncodeError on every Windows
run — the reported crash;
* its audiomap write_text() pairs ensure_ascii=False with the default file
encoding, so a non-ASCII payload is unwritable there too;
* lint_source.py read_text() raises UnicodeDecodeError before any rule runs
when a Remotion source carries an em dash or a curly quote;
* gen-stroke-path.py reads an SVG font whose glyph keys ARE literal
characters, so a mis-decoded key stops matching the requested text.
Stdio is reconfigured to UTF-8 at import and every text-mode IO call names its
encoding. `errors` is carried across the reconfigure: it resets to "strict",
and CPython gives stderr "backslashreplace" on purpose so the diagnostic path
can never itself raise.
extract-audio-data.py also decoded ffmpeg's stderr strictly while reporting a
failure, which would bury the very error being reported on a Windows ffmpeg.
skills/python-encoding.test.mjs guards the class: it fails if any skill Python
script drops the stdio block or omits encoding= on a text-mode IO call. The
mode is read as a whole comma-delimited argument of mode characters only, so a
payload key like {"bpm": 120} cannot spell the check away.
Verified with a cp1252 stdio stream installed before module load, matching how
Windows starts the interpreter: pre-fix UnicodeEncodeError, post-fix both
glyphs present in the UTF-8 bytes. Not run on real Windows hardware.
Three R3 findings.
The redactor's segment classes were ASCII `\w`, so `/数据/客户/秘密视频.mp4` and
`/data/客户/secret.mp4` went out verbatim — and the generic redactor also feeds
CLI telemetry and producer observation messages, where no known-path list
compensates. Segments are now defined by their delimiters instead of an
alphabet, which is correct for every script by construction rather than
requiring Unicode classes to be kept correct. The bare-relative lookbehind had
the same ASCII assumption and let a match start mid-token, redacting
`客户/秘密/视频.mp4` to `客户[path]`; it is now a token boundary, and
bare-relative runs before absolute so it claims the whole token.
sanitizeProbeFailure cast the rejection reason to Error and read `.message`.
An injected probe can reject with anything, so `Promise.reject("failed")` gave
`undefined` and threw inside the redactor — converting a returned failure
result into a rejected promise. Normalized at the boundary, and
redactKnownPaths no longer throws on a non-string.
The contract only admitted .ts/.js/.mjs/.cjs, so it missed shipped shell and
Python callers. frame_strip.sh passed a user-controlled path as ffprobe's last
positional with no terminator; render-and-composite.sh had four more. Both
fixed, and the sweep now covers .py/.sh. Python list argvs are bracket
literals so they get the same position check; shell command lines get a
separate presence check, because checking position there needs a shell parser
— stated as the weaker guarantee it is rather than implied to be equal.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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.
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