yolodex

name: yolodex description: Train a custom YOLO object detection model from any YouTube gameplay video. Provide a video URL and target classes, and this skill handles the entire pipeline autonomously — frame extraction, AI-powered labeling, data augmentation, training, and evaluation with iterative improvement. user_invocable: true

Intake Flow

When the user wants to train a model, gather the following:

1. Video source (required): YouTube URL or local file path (e.g. /Users/me/Desktop/gameplay.mp4)
2. Project name (required): Short kebab-case name (e.g. "subway-surfers", "fortnite-clips"). Output goes to runs/<project>/
3. Target classes (required): What objects to detect (e.g. "players, weapons, vehicles")
4. Labeling mode (required): Ask the user which labeling method to use:
   • CUA+SAM (recommended): OpenAI CUA clicks on objects, SAM segments precise boundaries. Best accuracy. Requires OPENAI_API_KEY.
   • Gemini: Google Gemini native bounding box detection. Fast, good accuracy. Requires GEMINI_API_KEY.
   • GPT: GPT vision model returns bounding boxes via structured output. Simple fallback. Requires OPENAI_API_KEY.
   • Codex: Codex subagents use built-in image viewing and write YOLO labels directly. No API keys.
5. Target accuracy (optional, default 0.75): mAP@50 threshold
6. Parallel agents (optional, default 4): How many labeling subagents (GPT mode only)

After Gathering Config

1. Write the values to config.json:

   import json
   config = json.load(open("config.json"))
   config["project"] = "subway-surfers"  # output goes to runs/subway-surfers/
   config["video_url"] = "<user's url or local path>"
   config["classes"] = ["player", "weapon", ...]
   config["label_mode"] = "cua+sam"  # or "gemini" or "gpt" or "codex"
   config["target_accuracy"] = 0.75
   config["num_agents"] = 4
   json.dump(config, open("config.json", "w"), indent=2)
   

2. Then execute the pipeline phases in order by following the iteration logic in AGENTS.md:

   • uv run .agents/skills/collect/scripts/run.py
   • Labeling (based on mode):
     • CUA+SAM: uv run .agents/skills/label/scripts/label_cua_sam.py
     • Gemini: uv run .agents/skills/label/scripts/label_gemini.py
     • GPT (parallel / call subagent): bash .agents/skills/label/scripts/dispatch.sh
     • Codex (parallel / no-key): bash .agents/skills/label/scripts/dispatch.sh
     • GPT (single): uv run .agents/skills/label/scripts/run.py
   • uv run .agents/skills/augment/scripts/run.py
   • uv run .agents/skills/train/scripts/run.py
   • uv run .agents/skills/eval/scripts/run.py

3. Check runs/<project>/eval_results.json — if accuracy < target, re-label failures and retrain.

Autonomous Mode

For fully autonomous execution, run: bash yolodex.sh This is a Ralph-style loop that iterates until target accuracy is reached.

Prerequisites

• OPENAI_API_KEY environment variable (for CUA+SAM and GPT modes)
• GEMINI_API_KEY or GOOGLE_API_KEY (for Gemini mode)
• No API key required when using label_mode=codex + dispatch.sh
• yt-dlp and ffmpeg installed
• uv for Python dependency management
• codex CLI (optional, for parallel subagent dispatch)