name: short-publish
description: End-to-end workflow for turning a local video into transcripts, burned subtitles, and scheduled multi-network posts via Postiz MCP tools. Use when given a video path and publication date/time to transcribe, create copy for LinkedIn/X/IG/YouTube, upload the subtitled MP4, and schedule the content with postiz_mcp.

Short Publish

Overview

This skill automates the complete "video → subtitles → Postiz" pipeline: run Whisper-based transcription, burn subtitles with the bundled Python script, turn the transcript into a multi-platform copy block, and program four social channels (YouTube, LinkedIn, X, Instagram) through the Postiz MCP integration.

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: always use the MCP tools (postiz-upload-file, postiz-create-post) exposed by postiz_mcp. Never fall back to the Postiz CLI.
• 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
2. Confirm the provided PATH exists; stop with a descriptive error if not.

3. Resolve DATETIME to an ISO timestamp. Use date -j -f or another deterministic macOS command when the input is natural language so Postiz receives an unambiguous value.

4. Transcribe and burn subtitles

5. Run the bundled helper: python3 scripts/transcribe_burn.py "$PATH".
6. Outputs (all written next to the original video):
   • <stem>.srt, <stem>.ass, <stem>.txt, <stem>_caption.txt, <stem>_subtitled.mp4.

7. 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.

8. Generate the social copy

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

10. 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.
    ```

11. 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.

12. Upload the subtitled video

13. Use postiz-upload-file with the _subtitled.mp4 path. Capture the returned public URL; Postiz expects this URL inside the images array for all channel posts.

14. Schedule the four posts via postiz-create-post

15. Integrations come from ~/.config/skills/config.json under postiz.groups.short_publish.
16. Use x-es by default for X unless the user explicitly asks for x-en.
17. Payload guidelines:
    • content: the copy block described above.
    • images: ["<uploaded_mp4_url>"] so Postiz attaches the video.
    • scheduledDate: resolved ISO timestamp based on DATETIME.

18. YouTube requires an explicit title and defaults to type=public; ensure the MCP call includes the generated title field.

19. Report completion

20. Confirm each scheduled post by echoing the returned IDs and their scheduled time in CET (UTC+01:00 during winter). Example: YouTube cm... → 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.