tml

{
    "name": "tml",
    "version": "0.1.4",
    "description": "tuneml — a read-first advisor for training and evaluating neural networks well: the scientific method for tuning, and the speed↔quality frontier, in one place. Prices every candidate change in time-to-target-quality rather than throughput, grades the evidence behind it, and names what it would silently break. Five skills: /tml:audit reads a pipeline and emits ranked, evidence-graded, priced findings across the algorithmic, systems, protocol and architecture tiers; /tml:plan fixes an operating point and a step budget under an explicit quality floor; /tml:round designs the next round of experiments, classifying hyperparameters as scientific, nuisance or fixed; /tml:analyze extracts insight from results and returns a variance-aware adopt verdict; /tml:review adversarially checks a proposed or applied change. Runs local or remote, and analyses results it did not produce. Supersedes parml. No hooks, no MCP, no settings patching.",
    "author": {
        "name": "Emanuele Ballarin",
        "email": "[email protected]"
    },
    "homepage": "https://github.com/emaballarin/ccplugins/tree/main/plugins/tuneml",
    "repository": "https://github.com/emaballarin/ccplugins",
    "license": "MIT",
    "keywords": [
        "tuneml",
        "hyperparameter-tuning",
        "training-efficiency",
        "experiment-design",
        "tradeoff",
        "profiling",
        "ablation",
        "benchmark",
        "machine-learning",
        "deep-learning"
    ]
}