Yolo Master Agent

YOLO-Master Agent Skill

Use This Skill

• train, val, predict, track, export, benchmark, tune
• LoRA diagnose, PEFT compare, MoE diagnose/prune
• Multimodal visual inference and batch evaluation
• End-to-end orchestration via yolo.pipeline.experiment

Execution Rule

If yolo version fails, run pip install -e . first. Prefer the yolo CLI over raw Python API for supported commands. Use the bundled dispatcher for deterministic, structured runs:

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python agent/scripts/run_yolo_master_skill.py --json '{"skill":"yolo.train","inputs":{"model":"yolo11n.pt","data":"coco8.yaml"},"params":{"epochs":1,"imgsz":32}}'

On Apple Silicon, the dispatcher defaults heavy compute (train, val, benchmark, predict, track) to device=mps. Override with runtime.device or params.device. If MPS/CUDA fails, the dispatcher retries once on CPU and returns a recovery record.

Use policy.dry_run=true for cheap coverage before real runs. For long jobs, set policy.async=true on train/tune/pipeline to submit a subprocess job.

Workflow

1. Ensure yolo CLI is available; install if missing.
2. Dispatch the task via run_yolo_master_skill.py with appropriate skill, inputs, and params.
3. Return structured artifacts, metrics, and next actions from the dispatcher response.

Validation

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python agent/scripts/validate_yolo_master_skill.py --suite quick --pretty --summary-only

quick = fast-smoke + dry-run + contract. Use --suite all for the full regression pass.

References

• Skill architecture — skill registry, request/response contract, execution logic
• Multimodal inference — VLM/LLM parameters, prompt templates, fusion, open-world taxonomy
• Multimodal evaluation — batch evaluation, fusion guardrails, metric guardrails
• AutoTrain loop — validator suites, case pack, tiered coverage
• Pipeline & PEFT — pipeline experiments, release audit, LoRA diagnose, PEFT compare
• Manual probes — extended CLI suite, direct train/val, environment doctor
• Thinking with image — VLM visual reasoning, marked-image prompting, crop/zoom search
• Open-world taxonomy assets in assets/open-world-taxonomy — LVIS 1203 and V3Det 13204 category lists

Guardrails

• New Ultralytics CLI args must go through params dict, not as top-level dispatcher keys.
• Consume evaluation in addition to metrics when judging train/val runs.
• Prefer the recovery field over raw stderr when a run auto-falls back from MPS/CUDA to CPU.
• Treat UI launchers and research scripts as launcher-style skills, not plain sync functions.