antoniolg/short-publish

Short Publish

Overview

This skill automates the complete "video → subtitles → PostFlow" pipeline: run Whisper-based transcription, burn subtitles with the bundled Python script, turn the transcript into a multi-platform copy block, and schedule social posts through the PostFlow CLI.

Inputs & Prerequisites

• Arguments:
  • PATH – absolute path to the source video (MOV/MP4/etc.).
  • DATETIME – publication date/time (accepts natural language like "tomorrow 09:00"). Use date to confirm the current timestamp if needed.
• Tooling: use the postflow CLI (postflow media upload, postflow posts create) and refer to postflow-cli for command details.
• Script dependency: scripts/transcribe_burn.py wraps Whisper, ffmpeg, and auto-gain. Requires Python 3.8+, ffmpeg, and openai-whisper installed for the user; no extra configuration is needed inside this skill.
• Timezone: default to Europe/Madrid. In winter assume UTC+01:00 (CET) when presenting final schedules if the date command does not provide the offset.

Workflow

1. Collect inputs

   • Confirm the provided PATH exists; stop with a descriptive error if not.
   • Resolve DATETIME to an ISO timestamp. Use date -j -f or another deterministic macOS command when the input is natural language so PostFlow receives an unambiguous value.

2. Transcribe and burn subtitles

   • Run the bundled helper: python3 scripts/transcribe_burn.py "$PATH".
   • Outputs (all written next to the original video):
     • <stem>.srt, <stem>.ass, <stem>.txt, <stem>_caption.txt, <stem>_subtitled.mp4.
   • The _subtitled.mp4 is the media you will upload; everything else is transient reference material. Remove the generated artifacts (srt/ass/txt/caption/mp4_subtitled/normalized wav) once they have been read and the upload succeeds—never delete the original video.

3. Generate the social copy

   • Read <stem>.txt for the full transcript.

   • Apply the exact copywriting prompt below to the transcript; do not improvise structure or tone beyond the template.

     Act as an expert LinkedIn copywriter building authority content.
     Transform the TRANSCRIPT into a case-study or practical-lesson post with this structure:
     1. Hook headline with a leading emoji.
     2. 2-3 sentence context introducing the situation.
     3. Structured core (use 1️⃣/2️⃣/3️⃣ or ✅ and bold keywords per line).
     4. Closing takeaway line.
     5. Optional P.S. only when the transcript mentions an offer/event.
     
     Style rules: short paragraphs (1-2 lines), intentional emoji usage, no invented facts, stay faithful to the transcript.
     

   • Reuse the single output block verbatim for LinkedIn, X, and Instagram, and as the YouTube description (light line breaks allowed). Craft a YouTube title ≤100 characters from the same content.

4. Upload the subtitled video

   • Upload the _subtitled.mp4 and capture media_id:

     postflow --json media upload --file "<stem>_subtitled.mp4" --kind video
     

   • Use the returned id as media_id for all posts.

5. Schedule posts via postflow posts create

   • Accounts come from ~/.config/skills/config.json under postflow.groups.short_publish and postflow.accounts.
   • For X aliases, use postflow.defaults.x (default x-es) unless the user explicitly requests another account.
   • For each account, create a scheduled post with the same copy block and the uploaded media:

     postflow posts create \
       --account-id <acc_id> \
       --text "<copy_block>" \
       --media-id <media_id> \
       --scheduled-at <ISO8601>
     

   • If a thread is needed, use --segments-json instead of --text.

6. Report completion

   • Confirm each scheduled post by echoing returned IDs and scheduled time in CET (UTC+01:00 during winter). Example: LinkedIn pst_... → 2025-01-11T10:00:00+01:00 (CET).

Resources

• scripts/transcribe_burn.py: Whisper + ffmpeg pipeline used in Step 2. Copy-safe to reuse elsewhere but do not edit unless the video workflow changes. Running the script produces all intermediate assets and the burned MP4 referenced throughout the workflow.