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video-scene-analyzer

📁 soheik/agent-skills 📅 Today
3
总安装量
3
周安装量
#61716
全站排名
安装命令
npx skills add https://github.com/soheik/agent-skills --skill video-scene-analyzer

Agent 安装分布

opencode 3
claude-code 3
github-copilot 3
codex 3
kimi-cli 3
gemini-cli 3

Skill 文档

Video Scene Analyzer

Detect scene boundaries in video files and generate structured JSON with content summaries, production tags, and a visual asset library per scene.

Prerequisites

Requires ffmpeg and ffprobe. Verify:

ffmpeg -version && ffprobe -version

If missing, inform the user and stop.

This skill bundles its own videx script at scripts/videx (relative to SKILL.md). Resolve the full path:

VIDEX="$(dirname "$(realpath "<path-to-SKILL.md>")")/scripts/videx"

Workflow Overview

Two-pass process:

  1. Pass 1 — Overview: Extract low-res frames, read them all, identify scene boundaries
  2. Pass 2 — Deep-dive: Extract high-res frames per scene, generate detailed metadata
  3. Output: Write analysis.json + conversation summary

Pass 1: Overview Scan

Step 1: Get video metadata

ffprobe -v error -show_entries format=duration,size -show_entries stream=codec_name,width,height,r_frame_rate -of default=noprint_wrappers=1 <video>

Record duration, resolution, fps for the output JSON.

Step 2: Extract overview frames

Choose interval based on duration:

Duration Interval Command
< 5 min 2s $VIDEX overview <video> 2 320 0.5
5-30 min 5s $VIDEX overview <video> 5 320 0.5
> 30 min 10s $VIDEX overview <video> 10 320 0.5

Use triplet=0.5 (wider spread) to make transitions visible.

Step 3: Read all overview frames

Read every _b (center) frame chronologically using the Read tool. Read in batches of 20-30 if many frames.

For timestamps where the _b frame looks like a transition (blur, blend, fade), also read the _a and _c frames.

Step 4: Detect scene boundaries

Compare consecutive _b frames. A scene boundary exists when:

  • Hard cut: Completely different content between consecutive frames
  • Dissolve/fade: Frame shows blending or fade to/from black/white
  • Major change: Same subject but clearly different setting, angle, or composition

Heuristic: when uncertain, prefer splitting. Users can merge; they cannot split what was missed.

Record for each boundary:

  • Approximate timestamp (midpoint between the two sample points)
  • Transition type (cut, dissolve, fade-to-black, fade-from-black, fade-to-white, fade-from-white, wipe)

The first frame always starts Scene 1. The last sample point ends the final scene (use video duration).

Step 5: Report scene list

Show the user a scene list before proceeding:

Scene 1: 0:00 - 0:04 (cut)
Scene 2: 0:04 - 0:07 (dissolve)
...

Ask if they want to adjust boundaries or proceed.

Pass 2: Scene Deep-Dive

Step 6: Extract scene frames

For each scene, extract at 1280px:

$VIDEX range <video> <start>-<end> --triplet=0.2

Short scenes (< 2s): add --fps=5 Long scenes (> 30s): default 2fps is fine.

Step 7: Analyze each scene

Read all extracted frames for the scene. Determine:

Content summary: 2-4 sentences. Subjects, actions, setting, props, visible text.

Production tags:

Tag Values
camera_angle eye-level, low-angle, high-angle, bird’s-eye, dutch-angle, over-the-shoulder, pov
shot_size extreme-wide, wide, medium-wide, medium, medium-close-up, close-up, extreme-close-up
camera_movement static, pan-left, pan-right, tilt-up, tilt-down, zoom-in, zoom-out, dolly-in, dolly-out, tracking, handheld, crane, steadicam
color_tone warm, cool, neutral, desaturated, high-contrast, low-contrast, monochrome, neon, pastel, earth-tones
lighting natural, artificial, high-key, low-key, backlit, side-lit, top-lit, silhouette, mixed
tempo static, slow, moderate, fast, frenetic

Text overlay:

  • placement: none, lower-third, centered, top, full-screen, watermark
  • content: actual text visible, or null

Transitions:

  • in: how scene begins (none for first scene)
  • out: how scene ends (none for last scene)

If a tag changes mid-scene, use the dominant value and note the change in notes.

Step 7b: Build asset library

While analyzing scenes, catalog every distinct visual asset across the entire video. For each asset, record:

Field Description
id Kebab-case unique identifier (e.g., student-summer, logo-brand)
type character, background, icon, ui-screen, photo, product, logo, decoration, text-graphic, effect
label Human-readable one-line description with key visual traits
style anime-illustration, flat-design, realistic-photo, 3d-render, hand-drawn, typography, mixed
appears_in Array of scene numbers where this asset appears

Guidelines:

  • Characters with costume changes get separate entries (e.g., student-summer, student-winter)
  • Same icon in multiple scenes = one entry with all scene numbers
  • Only list distinctive backgrounds (skip generic white/black)
  • label should be concise but include enough detail to identify the asset (color, pose, size, distinguishing features)

See references/schema.md for the full type reference and examples.

Step 8: Write analysis.json

Assemble the complete JSON per the schema in references/schema.md.

Write to: ./videx-out/<video-name>/analysis.json

Step 9: Conversation summary

After writing JSON, provide:

  1. Total scenes and video duration
  2. One-line summary per scene with timestamps
  3. Asset library summary (total count, type breakdown)
  4. Notable production patterns across the video

Edge Cases

  • < 10 seconds: Use 1-second interval. Single scene is valid.
  • > 1 hour: Use 10-second intervals. Read overview frames in batches.
  • Static content (slideshow/screencast): Slide changes = scene boundaries. Note camera_movement: static.
  • Ambiguous boundaries: Prefer splitting over merging.

Output Location

./videx-out/<video-name>/
├── overview/          (Pass 1)
├── range_*/           (Pass 2)
└── analysis.json      (final output)
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