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Use when analyzing remote MLflow tracking servers from the CLI to inspect experiments, runs, metrics, params, tags, and artifacts.
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Baseado na classificação ocupacional SOC
Use when opening a PR and driving it to green CI — push the branch, write a high-level PR description, watch checks, fix failures, and keep looping until green; also use when an already-open PR gets new commits.
Use when the user starts, resumes, switches, saves, or recalls a named task or ongoing work across sessions (e.g. "remember this", "continue X", "what was I doing on Y", "track this task").
Git commit workflow. Load when finishing any code-change task (after verification) to commit and push, or when explicitly staging/committing.
Use when modifying or adding a transform, loss, model, method, package, task-model, config, docs page, or example in a Lightly AG repo (lightly-ssl or lightly-train) and you need repo-specific conventions.
Spawn a subagent to run a task. Use when you want to delegate work to a separate pi instance.
Use when configuring or replicating LT-DETR v2 (ltdetrv2-s/m/l/x) COCO benchmarks via the lightly-train wrapper — model alias map, global vs per-rank batch size, recipe invariants, ECDet config mapping, cluster pins, and common gotchas.
| name | mlflow-cli |
| description | Use when analyzing remote MLflow tracking servers from the CLI to inspect experiments, runs, metrics, params, tags, and artifacts. |
MLFLOW_TRACKING_URI points to the target server; if it is missing, ask for the server URL before querying.--help / subcommand help first; use docs only to fill gaps.mlflow is not available, try the project environment (uv run mlflow --help or .venv/bin/mlflow --help) before giving up.--output json where supported and mlflow experiments csv for comparisons.mlflow experiments search, then confirm it with mlflow experiments get -x <id> or -n <name>.mlflow runs list --experiment-id <id>; use --view active_only|deleted_only|all when needed.mlflow runs describe --run-id <run_id> to capture params, metrics, tags, artifact roots, and metric history context.mlflow artifacts list -r <run_id> [-a <path>], then download interesting paths with mlflow artifacts download.mlflow experiments csv -x <id> -o <file> for offline sorting/comparison.MLmodel, requirements.txt, conda.yaml, plots, checkpoints, logs)