eyeroll 0.4.3

AI eyes that roll through video footage — watch, understand, turn into code actions.

eyeroll

AI eyes that roll through video footage — watch, understand, act.

eyeroll is a Claude Code plugin that analyzes screen recordings, Loom videos, YouTube links, and screenshots, then helps coding agents fix bugs, build features, and create skills.

Install

# Add the plugin to Claude Code
/plugin marketplace add mnvsk97/eyeroll
/plugin install eyeroll@mnvsk97-eyeroll

# Install the CLI
pip install eyeroll[gemini]      # Gemini Flash API (recommended)
pip install eyeroll[openai]      # OpenAI GPT-4o + OpenRouter/Groq/Grok/Cerebras
pip install eyeroll              # Ollama only (local, no API key)
pip install eyeroll[all]         # everything

Setup

/eyeroll:init

Picks your backend, configures API key, and generates codebase context — all in one step.

Commands

Command	What it does
/eyeroll:init	Set up eyeroll — pick backend, configure API key, generate .eyeroll/context.md
/eyeroll:watch <url>	Analyze a video and present a structured summary
/eyeroll:fix <url>	Watch a bug video → diagnose → fix the code → raise a PR
/eyeroll:history	List past video analyses

Usage

In Claude Code

You: /eyeroll:watch https://loom.com/share/abc123
     → Analyzes video, presents: what's shown, the bug, key evidence, suggested fix

You: /eyeroll:fix https://loom.com/share/abc123
     → Watches video, greps codebase, finds the bug, fixes it, raises a PR

You: watch this tutorial and create a skill from it: ./demo.mp4
     → video-to-skill activates, watches video, generates SKILL.md

You: /eyeroll:history
     → Lists past analyses with timestamps and sources

Standalone CLI

eyeroll watch https://loom.com/share/abc123
eyeroll watch ./bug.mp4 --context "checkout broken after PR #432"
eyeroll watch ./bug.mp4 -cc .eyeroll/context.md --parallel 4
eyeroll watch ./bug.mp4 --backend ollama -m qwen3-vl:2b
eyeroll watch ./bug.mp4 --backend groq
eyeroll watch ./bug.mp4 --backend openrouter -m anthropic/claude-3.5-sonnet
eyeroll watch ./bug.mp4 --backend openai-compat --base-url https://my-server/v1
eyeroll history

How it works

/eyeroll:watch https://loom.com/share/abc123
    ↓
1. Preflight check (verify backend is reachable, detect capabilities)
    ↓
2. Download video (yt-dlp)
    ↓
3. Choose strategy:
   - Gemini API key: direct upload via File API (up to 2GB)
   - Gemini service account: direct upload (up to 20MB)
   - OpenAI / OpenRouter / Groq: multi-frame batch (all frames in one call)
   - Ollama: frame-by-frame (one frame per call)
    ↓
4. Transcribe audio if present
    ↓
5. Cache intermediates (reuse on next run)
    ↓
6. Synthesize report with codebase context:
   - Metadata: category, confidence, scope, severity, actionable
   - Bug Description + Reproduction Steps
   - Fix Directions (Visible / Codebase-informed / Hypothesis)
   - Search patterns for the coding agent
    ↓
7. Present summary to user
    ↓
/eyeroll:fix goes further:
   → grep codebase → read files → implement fix → run tests → PR

Backends

Backend	Strategy	Audio	API Key	Cost	Best for
gemini	Direct upload (up to 2GB)	Yes	GEMINI_API_KEY	~$0.15	Best quality
openai	Multi-frame batch	Whisper	OPENAI_API_KEY	~$0.20	Existing OpenAI users
ollama	Frame-by-frame	No	None	Free	Privacy, offline
openrouter	Multi-frame batch	No	OPENROUTER_API_KEY	varies	Model variety
groq	Multi-frame batch	No	GROQ_API_KEY	cheap	Low latency
grok	Multi-frame batch	No	GROK_API_KEY	varies	xAI models
cerebras	Multi-frame batch	No	CEREBRAS_API_KEY	cheap	Fast inference
openai-compat	Multi-frame batch	No	any env var	varies	Custom/self-hosted endpoints

Ollama auto-installs if not found (macOS/Linux).

Codebase context

/eyeroll:init generates .eyeroll/context.md — a summary of your project that eyeroll uses to ground its analysis in real file paths instead of hallucinating them.

Without context, all file paths in the report are labeled as hypotheses.

Caching

eyeroll caches frame analyses and transcripts in .eyeroll/cache/. Same video = no re-analysis. Different --context re-runs only the cheap synthesis step.

eyeroll watch video.mp4                    # full analysis (~15s)
eyeroll watch video.mp4 -c "new context"   # instant — cached frames
eyeroll watch video.mp4 --no-cache         # force fresh

Plugin structure

eyeroll/
  commands/              ← slash commands
    init.md              ← /eyeroll:init
    watch.md             ← /eyeroll:watch
    fix.md               ← /eyeroll:fix
    history.md           ← /eyeroll:history
  skills/                ← background skills
    video-to-skill/      ← activated by "create a skill from this video"
  eyeroll/               ← Python CLI package
    cli.py, watch.py, analyze.py, extract.py, backend.py, history.py
  tests/                 ← 269 unit + 8 integration tests

Supported inputs

Input	Formats
Video	.mp4, .webm, .mov, .avi, .mkv, .flv, .ts, .m4v, .wmv, .3gp, .mpg, .mpeg
Image	.png, .jpg, .jpeg, .gif, .webp, .bmp, .tiff, .heic, .avif
URL	YouTube, Loom, Vimeo, Twitter/X, Reddit, 1000+ sites via yt-dlp

Development

git clone https://github.com/mnvsk97/eyeroll.git
cd eyeroll
pip install -e '.[dev,all]'
pytest                                                    # unit tests
pytest tests/test_integration.py -v -m integration        # real API tests

License

MIT