Video Analysis

Follow shared release-shell rules in:

• postplus-shared release-shell rules

Use this skill for video-level analysis after metadata research has already narrowed a candidate set.

Follow shared routing and guidance rules in:

• postplus-shared research preferences

This skill is usually downstream of platform research, not the default first step for broad TikTok discovery.

Use For

• upload local video files to the official Gemini Files API
• run Gemini 3.1 Pro Preview on video inputs
• use a stable TikTok/Reels analysis prompt
• request structured JSON output
• keep analysis linked to source metadata such as TikTok URL, video id, or dataset row

Trigger Signals

Use this skill when the user asks for things like:

• why a video works
• hook or structure breakdown
• spoken-line or CTA analysis
• shot-level decomposition
• visual execution analysis
• adaptation or recreation guidance based on actual videos

Do not use this skill as a substitute for broad TikTok trend discovery when no shortlist exists yet.

Core Resources

• Official source library: references/core-sources.md
• TikTok analysis prompt: references/tiktok-video-analysis-prompt.md
• JSON schema: references/tiktok-video-analysis-schema.json
• Downloader: ../../00-core/shared-runtime/scripts/download_videos_from_manifest_with_ytdlp.mjs
• Batch runner: scripts/run_video_analysis_batch.mjs
• Manifest builder: scripts/build_manifest_from_master_table.mjs
• Backfill helper: scripts/backfill_master_table_with_script.mjs

Workflow

For this workspace, use this skill after:

1. metadata research identifies high-value videos
2. the actual video files are available locally

If the local video files are missing, do not stop at metadata. Recover the source video first, then run analysis.

For this workspace, a practical recovery path is:

1. try to locate previously downloaded local videos
2. if downloader dependencies are missing, follow the postplus-shared Local Dependency Bootstrap Rule first
3. if still missing, download from the TikTok web URL with yt-dlp
4. save files under a stable workspace path
5. only then call the Gemini analysis scripts

Do not start with full-market video analysis. First shortlist, then analyze.

If the user request is broad or ambiguous, ask one short question before running:

• "你是想先找出值得看的爆款样本，还是已经有视频要我直接拆 hook、结构和镜头？"

If the user appears to want a broader TikTok research outcome, proactively mention that skills/20-research/tiktok-research can first build the shortlist this skill should analyze.

Environment

Do not store secrets in this repo.

In the product shell:

• follow postplus-shared release-shell rules
• this skill requires python3, yt_dlp, and ffprobe; follow the postplus-shared Local Dependency Bootstrap Rule before analysis
• if the required Gemini capability is missing, or local dependency bootstrap fails, or the script returns a stable network/proxy/DNS error, stop immediately and report that failure

Default Model

• gemini-3.1-pro-preview

Do not use gemini-3-pro-preview; it has been shut down.

First Run

Use a single local video and keep the first request simple:

node skills/40-creative/video-analysis/scripts/run_video_analysis_batch.mjs \
  --download-report <work-folder>/.postplus/download-report.json \
  --output-dir <work-folder>/.postplus/video-analysis-results \
  --concurrency 1 \
  --model gemini-3.1-pro-preview

The download report should contain at least:

{
  "results": [
    {
      "sourceId": "demo-1",
      "sourceUrl": "https://www.tiktok.com/@demo/video/1",
      "filePath": "/abs/path/to/video.mp4",
      "success": true
    }
  ]
}

Batch Guidance

When scaling to many videos:

• keep provider calls concurrent but bounded
• start with concurrency 2-4
• prefer inline for short TikTok clips under 20MB when the network path to Files API is unreliable
• persist one JSON result per source video
• include source ids and source URLs in every result
• retry upload and generate calls with bounded backoff
• separate upload failures from model failures

Fallback Strategy

For this workspace, the preferred first-version strategy is:

1. whole video inline if under 20MB
2. compress if over 20MB
3. segment into overlapping clips if still over 20MB
4. aggregate clip analyses back into one video-level result
5. join final analyses back to the source metadata dataset

Keep These Assets

Do not treat downloaded videos as disposable temp files if they were expensive to source.

When a benchmark set matters, keep:

• the local video file
• the analysis JSON
• the manifest or URL list that can restore the file later

If you only keep the metadata table, you may lose the ability to reproduce shot-level analysis later.

Shot-Level Backfill

If shot-level fields were generated by Gemini but not preserved in the master table, backfill them instead of creating duplicate records.

Use:

node skills/40-creative/video-analysis/scripts/backfill_master_table_with_script.mjs \
  --master "reports/video-master-table.csv" \
  --analysis-dir /path/to/analysis-dir

This updates matching source ids in the existing master table and preserves the single-table workflow.

Batch example:

node skills/40-creative/video-analysis/scripts/run_video_analysis_batch.mjs \
  --download-report /path/to/download-report.json \
  --output-dir <work-folder>/.postplus/video-results \
  --concurrency 2 \
  --model gemini-3.1-pro-preview

Always Keep

Never treat video analysis as isolated output. Always keep these fields:

• sourceId
• sourceUrl
• sourceMetadataPath or dataset path
• videoFilePath
• model
• promptVersion

That makes it possible to join Gemini output back to TikTok metadata later.