技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
observers-and-logging【数据与研究】Store and inspect Sacred run metadata, artifacts, resources, metrics, and external observer integrations.
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Use this sub-skill when the task is about recording, inspecting, or troubleshooting Sacred run metadata: observers, local run directories, metrics, _run.info, resources, artifacts, Python logging, and optional external storage/notification integrations.
run.json, config.json, info.json, cout.txt, metrics.json, artifacts, resources, and source snapshots.started_event, heartbeat_event, terminal events, resource/artifact events, log_metrics) or how observer priority and run IDs work.FileStorageObserver, MongoObserver, QueuedMongoObserver, SqlObserver, TinyDbObserver/TinyDbReader, QueueObserver, S3/GCS, Slack, Telegram, or Neptune-style integrations.@ex.main, ex.run, and basic Run lifecycle belong in the experiment-core sub-skill.configuration-and-cli sub-skill.reproducibility-and-capture sub-skill.FileStorageObserver cannot write, metrics are absent/out of order, or QueueObserver appears stuck.priority; the first observer that sees _id=None determines the run ID.FileStorageObserver for no-service local validation. It needs only a writable directory and is the verified path for this sub-skill.ex.info or _run.info, call ex.log_scalar/_run.log_scalar, or use open_resource, add_resource, and add_artifact while a run is active.info, captured output, result, and metrics during heartbeat events; finished runs receive a final heartbeat before terminal observer events.info, input files as resources, and scalar series as metrics.