About video-analysis
video-analysis is a skill for analyzing local video files, selecting native video understanding for smaller files and frame extraction plus transcription for larger ones. It is intended for agents that need to answer questions about a supplied video file.
Key features
- 01Select native or extraction mode by file size
- 02Analyze video with a video-capable model
- 03Extract keyframes using intervals or scene changes
- 04Transcribe extracted audio with Whisper
- 05Configure model and extraction limits
Use cases
- 01Describe events in a supplied video clip
- 02Analyze a large video through selected frames and transcript
- 03Adjust frame limits to control analysis cost
- 04Use a configurable model for video analysis
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ starchild-ai-agent/ official-skills --skill video-analysisAfter installing, type/video-analysisin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
analyze.pyconfig.yamlexports.pyDetails
From the project READMEVideo Analysis
Analyze video files using either native model understanding or frame extraction + transcription.
⚠️ URL input (YouTube / TikTok / IG / Apple Podcasts / Spotify / any link)? This skill takes a local file path. For a link, do NOT download the media. Route by input type, in order, and STOP at the first level that yields text:
- Podcast episode link or episode name (Apple Podcasts, Spotify, Overcast,
publisher site, or just "the a16z episode about X") →
web_searchfor the publisher's episode page / show notes / transcript page, thenweb-crawler.scrape_markdown(url)to read it. Most major shows publish text. - Video URL →
web-crawler.youtube_video(url)/youtube_transcript(url)(cloud captions, ~1 s, no download; TikTok hastiktok_transcript). web_fetchcame back empty (empty_extraction, JS-rendered page) → retry the same URL withweb-crawler.scrape_markdown— it renders JS. Never conclude "no transcript" from an empty plain fetch.- Nothing found → report exactly that and ask. Local download +
analyze_video()runs ONLY when the user explicitly asks to download the media, or hands you a file. It is not a fallback: datacenter IPs hit bot-checks and a 100 MB audio pull takes minutes and usually fails.
Transcript text is speech only — never name a speaker from it alone (see web-crawler's metadata-first rule).
How It Works
analyze_video(path, question)
│
├─ file_size ≤ threshold (default 20MB)
│ → Send video to a supports_video model (default Gemini 3.1 Flash Lite)
│ → Model sees full video natively (best quality)
│
└─ file_size > threshold
→ ffmpeg extracts keyframes (scene detection for long videos)
→ Whisper transcribes audio track
→ Returns frame image paths + transcript text
→ Agent feeds these to the current chat model
Quick Start
⚠️ Invocation — do NOT use dotted imports. The directory name contains a
hyphen (video-analysis), so from skills.video-analysis.exports import ...
is a Python syntax error (- is parsed as minus). This is true for every
hyphenated skill, not just this one. Use one of the two patterns below.
Pattern A — from workspace root (recommended for scripts):
cd /data/workspace/skills/video-analysis && \
python3 -c "from exports import analyze_video; \
import json; \
print(json.dumps(analyze_video('output/videos/clip.mp4', \
question='What happens in this video?'), ensure_ascii=False))"
Note: pass the video path workspace-relative (analyze.py resolves it
against WORKSPACE_DIR), even though you cd into the skill dir.
Pattern B — inside a starchild-clawd script:
from core.skill_tools import video_analysis
result = video_analysis.analyze_video("output/videos/clip.mp4",
question="What happens in this video?")
❌ Do NOT exec(open('skills/video-analysis/analyze.py').read()) — analyze.py
uses __file__ at import time, which is undefined under exec, so it crashes.
Load it by file path with importlib.util.spec_from_file_location if you must
avoid both patterns above.
# result keys (same for both patterns):
# Analyze a video — auto-selects native or extraction mode
# result = analyze_video("output/videos/clip.mp4", question="What happens in this video?")
# result keys:
# success: bool
# mode: "native" | "extraction"
#
# If mode == "native":
# analysis: str (model's text response)
# model: str (which model was used)
# tokens: {input, output, video, audio}
#
# If mode == "extraction":
# frame_paths: list[str] (workspace-relative paths to keyframe JPEGs)
# transcript: str | None (Whisper transcription text)
# frame_count: int
# duration_sec: float
Using the Exports
from core.skill_tools import video_analysis
# Full analysis (auto-selects mode)
result = video_analysis.analyze_video("output/videos/my_video.mp4", question="Describe this video")
# Check current config
config = video_analysis.get_config()
# Get video metadata without analyzing
info = video_analysis.get_video_info("output/videos/my_video.mp4")
# → {"duration": 45.2, "size": 12345678, "width": 1920, "height": 1080, "has_audio": true}
Native Mode (small videos)
For videos under the size threshold, the skill sends the full video to a model that supports native video input. The model sees every frame and hears the audio.
Default model: google/gemini-3.1-flash-lite — best price/quality for video.
Model benchmark (6MB clip, vs gemini-3.1-pro-preview baseline):
| Model | Tier | Cost | Time | Accuracy | Notes |
|---|---|---|---|---|---|
| google/gemini-3.1-flash-lite | budget | ~$0.0014 | 8.1s | ~88% | ⭐ Default — cheapest + fastest |
| google/gemini-3.5-flash | std | ~$0.0152 | 11.8s | ~85% | More detail, higher cost |
| qwen/qwen3.6-plus | budget | ~$0.0058 | 44.2s | ~95% | Accurate but slow |
| qwen/qwen3.6-flash | budget | ~$0.0027 | 16.6s | ~80% | Misreads subjects sometimes |
| google/gemini-3.1-pro-preview | std | ~$0.0199 | 19.7s | 100% | Baseline (best, most expensive) |
flash-lite identifies the full scene, action sequence, and transitions
correctly at ~14x lower cost than the Pro baseline. For maximum accuracy
(exact character names, fine detail), switch default_model to
gemini-3.1-pro-preview or gemini-3.5-flash in config/video-analysis.yaml.
Extraction Mode (large videos)
For videos over the size threshold, the skill extracts keyframes and transcribes audio:
- Short videos (≤60s): One frame every N seconds (default: 2s)
- Long videos (>60s): Scene-change detection picks visually distinct frames
- Audio: Extracted and sent to Whisper for transcription
- Max frames: Capped at 30 (configurable) to control cost
The agent receives frame image paths and transcript text, then feeds them to the current chat model as image attachments + context text.
Configuration
Edit config/video-analysis.yaml (in the workspace) to customize. This file
is created automatically on first use, only needs the keys you want to override,
and survives skill updates.
Do NOT edit
skills/video-analysis/config.yaml— that's the factory default and is overwritten on every skill auto-update. The user file overlays it.
Both the standalone skill and the chat "send a video" flow read this same config, so one edit changes the model everywhere. Available keys:
# Model for native video understanding
default_model: google/gemini-3.1-flash-lite
# Size threshold: native (≤) vs extraction (>)
# Set to 0 → always extraction. Set to 100 → always native.
native_size_limit_mb: 20
# Frame extraction settings
extraction:
max_frames: 30 # Max keyframes to extract
short_video_interval_sec: 2 # Frame interval for ≤60s videos
scene_threshold: 0.3 # Scene detection sensitivity (0.0-1.0)
transcribe_audio: true # Whether to Whisper-transcribe audio
Available Video Models
| Model | Alias | Tier | Notes |
|---|---|---|---|
| google/gemini-3.1-flash-lite | flash31 | budget | ⭐ Default, best price/quality |
| google/gemini-3.5-flash | gemini35 | standard | More detail, higher cost |
| google/gemini-3.1-flash-lite | flash31 | budget | Cheapest option |
| google/gemini-3.1-pro-preview | gemini | standard | Highest quality |
| qwen/qwen3.6-flash | qwenf | budget | Good alternative |
| qwen/qwen3.6-plus | qwen | budget | — |
| minimax/minimax-m3 | mm3 | standard | — |
| meta-llama/llama-4-maverick | maverick | standard | — |
| meta-llama/llama-4-scout | scout | budget | — |
| xiaomi/mimo-v2.5 | mimo | standard | — |
| z-ai/glm-5v-turbo | glm5v | standard | — |
| minimax/minimax-m2.7 | mm27 | budget | Audio-only, no image |
Agent Behavior
When the user provides a video file (via upload or file path) and the current chat model does NOT support video:
- Call
analyze_video(path, question). - If result mode is
"native"→ returnresult["analysis"]directly. - If result mode is
"extraction"→ useresult["frame_paths"]as image references andresult["transcript"]as context, then ask the current model to analyze based on the frames + transcript.
When the current model DOES support video, the backend handles it natively via Phase 1 (base64 content block injection) — no need for this skill.
Troubleshooting
| Problem | Fix |
|---|---|
| "File not found" | Check path is workspace-relative (e.g. output/videos/x.mp4) |
| Native mode returns error | Check default_model in config/video-analysis.yaml is valid |
| No audio transcription | Video may have no audio track; check has_audio in result |
| Too few frames extracted | Lower scene_threshold in config/video-analysis.yaml (e.g. 0.15) |
| Too many frames / high cost | Reduce max_frames or raise scene_threshold |