* Export videos for named platforms and check each file against its limits
Exports only knew aspect ratios, so a cut could still be rejected or
degraded on upload: too long for the platform, over its size limit, or
far off its loudness. export_video now takes platforms (TikTok,
Instagram Reels and Feed, YouTube Shorts and YouTube, Facebook Reels,
LinkedIn, X, Pinterest, Snapchat Spotlight), up to six targets with the
presets. Each platform is rendered to its frame with the export's fit,
its frame rate cap, a bitrate cap that also keeps the file under the
size limit, and its loudness target in one loudnorm pass.
Every render is probed and measured afterwards. A file over the size
limit is encoded once more at a bitrate scaled from what it averaged,
and audio more than 2 LU off the target gets one linear loudness pass.
The result is stored on the output as a pass, warn or fail check with
language-neutral issue codes. A platform the video is too long or too
short for is not rendered: the card lists it with the reason, the
other targets still render, and the bot is told to trim first.
The limits come from the earlier profile research, with its sources,
review date and unverified flag; content, disclosure and caption rules
are left out because they need inference or a review flow. Preset
definitions move to their own module so the platform schemas can use
their aspect ratios without an import cycle.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
* Add remove_silence step to edit_video
Talking-head clips are mostly cut by hand to drop pauses, and the bot had
no way to find them: it could only keep segments it guessed. The new
remove_silence step finds every pause quieter than threshold_db (default
-35 dB) and at least min_silence long (default 0.6 s), keeps padding
(default 0.12 s) on each side so word onsets survive, and cuts the rest
through the existing keep-segments graph, so picture and sound stay in
sync.
Steps run in order, so silence is detected on the timeline the steps
before it leave: the planner gets an analysis mode that sends that audio
through silencedetect into a null output, and ametadata prints the marks
to a file in the job directory. The detected speech is handed back to the
planner per step. Nothing silent makes the step a no-op; nothing left
fails with a clear error. At most 200 pieces are kept, bridging the
shortest pauses first, so the filter graph stays a sensible size.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
* Offer platforms in the media card's export menu and show their checks
The card's Export menu now lists the platforms first, by brand name and
untranslated, then the aspect ratios after a separator; the platform an
output was made for is left out, like its own shape. A platform export
shows the platform's name under the player, or, when its check did not
pass, one line for the first issue instead ("Too long for TikTok (max
10:00)"), in the warning or destructive color. Skipped platforms get
the same kind of line, and they stand in for the error of a job that
skipped every platform, so the card does not say the same thing twice
in two languages.
The E2E spec seeds a platform export with a failed check and a skipped
platform, and captures the card and the open menu.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
* Add analyze_video to find cut points for the bot
To cut a social video well the bot has to know where the cuts fit, and it
could not look at a video at all. analyze_video is a read-only media job:
one ffmpeg pass prints scene changes (scene score above 0.3, one per cut),
pauses (silencedetect with the remove_silence defaults) and short-term
loudness (ebur128) to files in the job directory, and a transcript made
from the video by transcribe (an SRT whose parent is the video) adds when
each line is spoken. The job stores nothing; its card just says done.
The result reaches the bot through the existing media job path: the job
always wakes the bot that started it (the tool has no continue_after) and
the summary carries a short legend plus compact JSON. The runtime's
summary limit grows to 12,000 characters and the summary is fitted into
it: at most 200 entries per list, then every list shrinks evenly, keeping
the strongest cuts, the longest pauses and the start of the transcript,
with omitted counts. The wake prompt no longer claims the card shows the
result when a job made no files.
Jobs read derived artifacts through a new fetchDerivedArtifact, scoped to
the job's user like fetchArtifact. A migration admits the operation in the
media_jobs check and adds the tool to existing Studio specialists that
still edit video. The web card gets an icon; no new copy.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
* fix(media): keep one skipped entry per platform on the card
A job that recovers after a crash runs again and reports the same
skipped platforms; the newest reason per platform now replaces the
earlier one instead of listing it twice.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
* chore(db): add the widened media job operation check as NOT VALID and validate it separately
Follows the repository's migration convention: the constraint commits
without scanning media_jobs, and a second migration validates it with a
lock that lets reads and writes continue.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u
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Co-authored-by: Claude <noreply@anthropic.com>
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