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: ```bash 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 ```bash 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](references/skill-architecture.md) — skill registry, request/response contract, execution logic - [Multimodal inference](references/multimodal-inference.md) — VLM/LLM parameters, prompt templates, fusion, open-world taxonomy - [Multimodal evaluation](references/multimodal-evaluation.md) — batch evaluation, fusion guardrails, metric guardrails - [AutoTrain loop](references/autotrain.md) — validator suites, case pack, tiered coverage - [Pipeline & PEFT](references/pipeline-peft.md) — pipeline experiments, release audit, LoRA diagnose, PEFT compare - [Manual probes](references/manual-probes.md) — extended CLI suite, direct train/val, environment doctor - [Thinking with image](references/thinking-with-image.md) — 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.