Menú
Analyze YOLO training runs — compares to baseline/best, checks per-class regression, analyzes training dynamics and tune convergence, writes actionable recommendations.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
| name | analyze-results |
| description | Analyze YOLO training runs — compares to baseline/best, checks per-class regression, analyzes training dynamics and tune convergence, writes actionable recommendations. |
Single-pass analysis run after each experiment or on-demand via /analyze.
Read:
results.csv or tune_results.csvexperiments/summary.md journal entriesexperiments/dataset_profile.yamltraining-plan.md (thresholds, goals, constraints)experiments/exp_NNN_name/train/results.csvexperiments/exp_NNN_name/results.csvmetrics.yaml — note that training dynamics analysis is unavailableFor tune runs, also check:
experiments/exp_NNN_name/tune/tune_results.csvComparison: Latest vs baseline AND current best (mAP50-95, per-class AP)
Per-class regression check: Flag any class exceeding training-plan.md threshold vs current best
Training dynamics (if results.csv available):
Tune analysis (if tune_results.csv available):
Diminishing returns detection (when 3+ experiments show <0.5% change):
Recommendations: Ordered by expected impact. Each cites evidence from the analysis.
Write experiments/analysis.md with findings and recommendations. Print top 3 findings + top 3 recommendations.
Findings also inform the journal entry's learning field and the next DIAGNOSE step in the reasoning loop.
analysis.md from the previous experiment or last explicit /analyze. It does NOT read analysis of the experiment that just ran before it has been analyzed.Set up autonomous training monitoring — creates cron jobs to track long-running training, auto-continue pipeline when training completes.
Audit YOLO dataset quality — class distribution, annotation quality, image stats, and improvement suggestions.
Orchestrate the full active learning loop: train, analyze, push to CVAT, wait for review, pull, merge, retrain.
Run autonomous YOLO training experiments — reads training-plan.md, assesses bottlenecks, acts strategically, and delegates HP optimization to model.tune().
Profile YOLO model inference speed, FPS, and size across image sizes and export formats.
Initialize a new YOLO project — detects your dataset's starting state and routes through the right tools.