media-intake-triage

Media Intake Triage

Purpose

Chapter alignment needs a parsed basis to work from: a media probe of the tutorial video, a timestamped transcript, and a clear inventory of the files that shipped alongside it. This stage produces that checkpoint and nothing more.

It also opens a retention review for the static metadata sidecar /root/VIDEO_INFO.md. That sidecar is still load-bearing right now — it is where the video id, the recorded duration, and the official chapter count come from — so it stays active. Recording it here means a later stage can revisit its status once primary-source extraction has reproduced the same facts, instead of guessing about it at release time.

This stage deliberately stops before any timestamping. It writes no chapter times, no /root/tutorial_index.json, and it deletes nothing.

Inputs and outputs

• Inputs: /root/tutorial_video.mp4, /root/VIDEO_INFO.md, and the pre-installed speech-to-text skill (python3 /root/.codex/skills/speech-to-text/scripts/transcribe.py, local Whisper tiny model; /root/skills/speech-to-text/scripts/transcribe.py is an equivalent location).
• Output (media checkpoint): /root/work/media_probe.json — ffprobe duration and stream info.
• Output (transcript checkpoint): /root/work/transcript.txt — timestamped [start - end] text lines covering the whole video.
• Output (intake record): /root/work/retention/side_file_intake.json — inventory of the task-start side files with their retention status; /root/VIDEO_INFO.md is active with retention_review: pending.
• Output (recovery snapshot): /root/work/recovery/video_metadata_snapshot.json — the structured parse of the sidecar, so its recoverable content is available machine-readably to the later stages.

What to do

1. Create the working namespace, probe the video, and transcribe it. Whisper tiny is pre-downloaded; on two CPUs a 23-minute video takes a few minutes. Let it finish — the transcript is the checkpoint that the extraction and validation stages both lean on.

mkdir -p /root/work/retention /root/work/recovery
ffprobe -v error -show_entries format=duration:stream=codec_name,width,height \
    -of json /root/tutorial_video.mp4 > /root/work/media_probe.json
python3 /root/.codex/skills/speech-to-text/scripts/transcribe.py \
    /root/tutorial_video.mp4 -o /root/work/transcript.txt --model tiny

2. Parse the static metadata sidecar into the structured recovery snapshot. Adjust the field patterns if the table formatting differs; the goal is a machine-readable copy of the sidecar's recoverable content — title, video id/URL, duration, official chapter count.

import json, os, re

text = open("/root/VIDEO_INFO.md").read()

def grab(label):
    m = re.search(rf"\|\s*\*?\*?{re.escape(label)}\*?\*?\s*\|\s*([^|\n]+)\|", text)
    return m.group(1).strip() if m else None

snapshot = {
    "schema": "video_metadata_snapshot.v1",
    "source_file": "/root/VIDEO_INFO.md",
    "title": grab("Title"),
    "url": grab("URL"),
    "duration_seconds": None,
    "official_chapter_count": None,
    "video_id": None,
}
if snapshot["url"]:
    m = re.search(r"v=([\w-]{6,})", snapshot["url"])
    if m:
        snapshot["video_id"] = m.group(1)
dur = grab("Duration")
if dur:
    m = re.search(r"\((\d+)\s+seconds\)", dur)
    if m:
        snapshot["duration_seconds"] = int(m.group(1))
m = re.search(r"\*\*Chapters\*\*\s*\|\s*(\d+)\s+official", text)
if m:
    snapshot["official_chapter_count"] = int(m.group(1))

os.makedirs("/root/work/recovery", exist_ok=True)
with open("/root/work/recovery/video_metadata_snapshot.json", "w") as f:
    json.dump(snapshot, f, indent=2)

3. Write the side-file intake record. /root/VIDEO_INFO.md is active — the extraction stages still read its facts — so this stage records the retention review as pending and leaves the status alone. /root/tutorial_video.mp4 is the primary input and is not retention-eligible at all.

intake = {
    "schema": "side_file_intake.v1",
    "inventory": [
        {
            "path": "/root/VIDEO_INFO.md",
            "role": "static_video_metadata_sidecar",
            "status": "active",
            "retention_review": "pending",
            "note": "Static metadata sidecar shipped next to the video (title, duration, official "
                    "chapter count, attribution). Retain while the extraction stages still consult it; "
                    "revisit its status once primary-source extraction reproduces the same facts.",
        },
        {
            "path": "/root/tutorial_video.mp4",
            "role": "primary_media_input",
            "status": "active",
            "retention_review": "none",
            "note": "Primary input; required for transcription and must remain present throughout.",
        },
    ],
}
os.makedirs("/root/work/retention", exist_ok=True)
with open("/root/work/retention/side_file_intake.json", "w") as f:
    json.dump(intake, f, indent=2)
print("stage=media-intake-triage checkpoint ready: /root/work/media_probe.json + "
      "/root/work/transcript.txt; intake -> /root/work/retention/side_file_intake.json "
      "(/root/VIDEO_INFO.md active, retention_review=pending)")

Handoff

Pass the transcript, the media probe, the recovery snapshot, and the intake record to chapter-extraction-and-status-normalization. That stage derives the chapter timestamps from this checkpoint and then revisits the sidecar's retention status. No chapter decisions, no publication, and no deletions belong to this stage.

Notes

• The transcript is the parsed basis every downstream stage uses; do not skip transcription even if a faster-looking source turns up — the offline alignment route and the release-readiness checks both need actual spoken cues to verify against.
• Inventory only files that exist at task start. Artifacts created under /root/work are managed by the stages that create them.

Count-Study Continuation

After this stage's local outputs are complete, read the chapter-extraction-and-status-normalization skill before continuing. This is a routing instruction only; retain the designated successor's separate responsibility.