# Video Analyse

> Analyse a video file by extracting timestamped frames and a Whisper transcript, then producing a structured analysis document. Use when the user asks to analyse a video, summarise a recording, get meeting notes from a video, or understand what happens in a video file. Triggers on phrases like "analyse this video", "video analysis", "what happens in this video", "summarise this recording", "meeting notes from this video", "video-analyse", "break down this video".

- Skill: `lfurze/video-analyse` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add lfurze/video-analyse`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lfurze/video-analyse/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: lfurze (https://skillmd.com/u/lfurze)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/lfurze/video-analyse

---


# Video Analyse

## Instructions

You are analysing a video file for the user. Follow these steps precisely.

### Step 1: Find the video

If the user provided a path as an argument, use that. Otherwise, look for video files in the current directory:

```bash
ls -1 *.{mp4,mov,mkv,avi,webm,m4v} 2>/dev/null
```

If multiple videos are found, ask the user which one. If none are found, ask for a path.

### Step 2: Check dependencies

Before running anything, verify the tools are available:

```bash
which ffmpeg && python3 -c "import whisper; print('whisper OK')"
```

If `ffmpeg` is missing: tell the user to run `brew install ffmpeg` (macOS) or `apt install ffmpeg` (Linux).
If `whisper` is missing: tell the user to run `pip install openai-whisper`.

### Step 3: Determine frame rate

The script at `~/.claude/skills/video-analyse/analyse_video.py` has auto mode built in. Unless the user specified a frame rate, use `--auto` which selects based on duration:

| Duration | FPM | Interval | Approx frames |
|----------|-----|----------|---------------|
| < 5 min | 2 | 30s | ~10 |
| 5–15 min | 1 | 60s | ~15 |
| 15–45 min | 0.5 | 2 min | ~15–22 |
| 45–90 min | 0.33 | 3 min | ~15–30 |
| 90+ min | 0.2 | 5 min | ~18–24 |

If the user asked for a specific rate, pass `--fpm <value>` instead.

### Step 4: Run the extraction pipeline

```bash
python3 ~/.claude/skills/video-analyse/analyse_video.py "<video_path>" --auto
```

This creates a `{video-stem}-analysis/` directory containing:
- `summary.md` — frame index and metadata
- `transcript.md` — timestamped Whisper transcript
- `frames/` — numbered JPG stills with timestamps

The script will take a while for long videos (transcription is the slow part). Let the user know it's running.

### Step 5: Read all outputs

Once the script completes, read everything into context:

1. Read `{video-stem}-analysis/summary.md`
2. Read `{video-stem}-analysis/transcript.md`
3. Read every frame image in `{video-stem}-analysis/frames/` (use the Read tool on each `.jpg` — Claude can see images)

This gives you full multimodal context: what was said (transcript) and what was shown (frames).

### Step 6: Write the analysis

Create `analysis.md` in the current working directory with the following structure:

```markdown
# Video Analysis: {filename}

**Duration:** {duration}
**Date analysed:** {today}

## Participants

List everyone visible or audible in the video. Note speaker identification confidence.

## Timeline Summary

For each major section/topic change, write a timestamped entry:

### [{timestamp range}] Section Title

What happened in this segment. Reference both audio (what was said) and visual (what was on screen) evidence.

> Key quote if relevant — [{timestamp}]

## Key Decisions

Bulleted list of decisions made (if applicable, e.g. for meetings).

## Action Items

- [ ] Action item with owner if identifiable — [{timestamp}]

## Visual Reference Points

Notable moments where the screen content is important:
- [{timestamp}] Description of what's shown and why it matters

## Whisper Accuracy Notes

Flag any sections where the transcript seems unreliable (garbled text, obvious misheard words, low-confidence sections).
```

Adapt the structure to fit the video type — a meeting needs decisions/action items, a tutorial needs step-by-step breakdowns, a presentation needs slide-by-slide coverage, etc.

### Step 7: Stay in context

After writing the analysis, tell the user it's done and that you have the full transcript + frames in context. Offer to:
- Answer questions about specific moments
- Find when a particular topic was discussed
- Extract more detail from any section
- Compare what was said vs what was shown on screen

## Options

- `--fpm <number>`: Override the auto frame rate (e.g. `--fpm 2` for one frame every 30s)
- `--whisper-model <size>`: Use a different Whisper model (`tiny`, `base`, `small`, `medium`, `large`). Default is `base`. Use `small` or `medium` for better accuracy on difficult audio.
- `--no-transcribe`: Skip Whisper transcription (frames only). Useful if the video has no speech or you only care about visuals.