القائمة
Use when analyzing remote MLflow tracking servers from the CLI to inspect experiments, runs, metrics, params, tags, and artifacts.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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)