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yolo-master-agent

Use when the user wants to run a YOLO-Master task (train/val/predict/track/export/benchmark) or use the Agent Skill dispatcher.

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Tencent/YOLO-Master

See how to install

Files included (93)

SKILL.md3.0 KB
assets/autotrain_cases.json35.0 KB
assets/autotrain_cases/01_yolo_benchmark.json733 B
assets/autotrain_cases/02_yolo_export.json696 B
assets/autotrain_cases/03_yolo_lora_train.json1.5 KB
assets/autotrain_cases/04_yolo_model_inspect.json751 B
assets/autotrain_cases/05_yolo_moe_diagnose.json728 B
assets/autotrain_cases/06_yolo_moe_prune.json738 B
assets/autotrain_cases/07_yolo_multimodal_evaluate.json3.7 KB
assets/autotrain_cases/08_yolo_multimodal_infer.json14.1 KB
assets/autotrain_cases/09_yolo_pipeline_experiment.json1.9 KB
assets/autotrain_cases/10_yolo_predict.json2.9 KB
assets/autotrain_cases/11_yolo_solutions_run.json669 B
assets/autotrain_cases/12_yolo_system.json2.6 KB
assets/autotrain_cases/13_yolo_track.json904 B
assets/autotrain_cases/14_yolo_train.json3.0 KB
assets/autotrain_cases/15_yolo_tune.json631 B
assets/autotrain_cases/16_yolo_ui_launch.json499 B
assets/autotrain_cases/17_yolo_val.json1.6 KB
assets/autotrain_cases/18_yolo_lora_diagnose.json780 B
assets/autotrain_cases/19_yolo_eval_peft_compare.json1.2 KB
assets/autotrain_cases/20_stability_check.json7.0 KB
assets/autotrain_cases/21_yolo_eval_sparse_sahi_compare.json736 B
assets/autotrain_cases/21_yolo_release_audit.json506 B
assets/autotrain_cases/22_yolo_job.json1.1 KB
assets/mini-detect/images/train/bus.jpg134.2 KB
assets/mini-detect/images/val/bus.jpg134.2 KB
assets/mini-detect/labels/train/bus.txt38 B
assets/mini-detect/labels/val/bus.txt38 B
assets/mini-detect/mini_detect.yaml83 B
assets/open_world_label_aliases.json603 B
assets/open-world-taxonomy/lvis_1203_classes.json73.6 KB
assets/open-world-taxonomy/lvis_1203_classes.txt19.3 KB
assets/open-world-taxonomy/sources.json780 B
assets/open-world-taxonomy/v3det_13204_classes.json814.3 KB
assets/open-world-taxonomy/v3det_13204_classes.txt205.9 KB
assets/prompts/vlm_coco_multitask.md3.9 KB
assets/prompts/vlm_open_world_caption_misses_compact.md1.4 KB
assets/prompts/vlm_open_world_detect_classify_compact.md1.6 KB
assets/prompts/vlm_open_world_detection_compact.md1.7 KB
assets/prompts/vlm_open_world_detection.md4.0 KB
metadata/openai.yaml3.1 KB
README.md7.5 KB
references/autotrain.md1.4 KB
references/manual-probes.md1.6 KB
references/multimodal-evaluation.md2.3 KB
references/multimodal-inference.md4.8 KB
references/pipeline-peft.md2.3 KB
references/skill-architecture.md42.9 KB
references/thinking-with-image.md9.1 KB
runtime/__init__.py60 B
runtime/cli/__init__.py73 B
runtime/cli/async_jobs.py7.5 KB
runtime/cli/compare_open_world_profiles.py9.5 KB
runtime/cli/contract.py13.3 KB
runtime/cli/core_handlers.py26.3 KB
runtime/cli/dataset.py9.6 KB
runtime/cli/device.py10.6 KB
runtime/cli/dispatcher.py13.3 KB
runtime/cli/executor.py16.8 KB
runtime/cli/job_handlers.py1.3 KB
runtime/cli/launcher_handlers.py6.0 KB
runtime/cli/lora_tools.py8.0 KB
runtime/cli/model_handlers.py1.8 KB
runtime/cli/moe_tools.py9.0 KB
runtime/cli/multimodal_handlers.py49.5 KB
runtime/cli/normalize.py10.4 KB
runtime/cli/peft_compare.py7.8 KB
runtime/cli/pipeline.py9.8 KB
runtime/cli/progress.py889 B
runtime/cli/regenerate_open_world_report.py8.5 KB
runtime/cli/release.py2.6 KB
runtime/cli/sahi_compare.py7.5 KB
runtime/cli/snapshot.py13.4 KB
runtime/cli/stability.py23.5 KB
runtime/cli/system_handlers.py8.3 KB
runtime/cli/validate.py41.7 KB
runtime/evaluation/__init__.py55 B
runtime/evaluation/metrics.py50.0 KB
runtime/multimodal/__init__.py64 B
runtime/multimodal/fusion.py29.6 KB
runtime/multimodal/providers/dashscope.yaml283 B
runtime/multimodal/providers/openai.yaml242 B
runtime/multimodal/runtime.py30.2 KB
runtime/multimodal/visual.py5.3 KB
runtime/open_world/__init__.py53 B
runtime/open_world/taxonomy.py31.4 KB
runtime/release.py24.9 KB
scripts/compare_open_world_profiles.py338 B
scripts/regenerate_open_world_report.py339 B
scripts/run_async_job.py1.1 KB
scripts/run_yolo_master_skill.py321 B
scripts/validate_yolo_master_skill.py319 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

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:

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

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

Validation

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.

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Install

Install with a command

npx skills add https://github.com/Tencent/YOLO-Master --skill yolo-master-agent

Paste it into your terminal and run it (requires Node.js). You can choose which agents to install it for, such as Claude Code or Codex.

Ask your agent

Please install the following Agent Skill.
https://github.com/Tencent/YOLO-Master/tree/main/agent

First review SKILL.md and the bundled files and confirm there is nothing suspicious (such as sending data externally or reading credentials). Then place it in ~/.claude/skills/yolo-master-agent/ for Claude Code, or ~/.agents/skills/yolo-master-agent/ for Codex.

Send this text to Claude Code or Codex as is. It checks the contents before installing.

View source on GitHub

Repository: Tencent/YOLO-Master
Stars: 765
Forks: 161
Category: Productivity
License: AGPL-3.0
Original language: English
Last updated: Oct 15, 2026

A skill is a set of instructions and scripts for an agent. cccskills does not guarantee that skills are safe. Review the contents before you install.