executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
experiment-coreBuild, run, and compose Sacred experiments with Experiment, Ingredient, Run, captured functions, commands, resources, artifacts, and programmatic execution.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when you need to design or modify the Python core of a Sacred 0.8.7 experiment: Experiment, Ingredient, Run, captured functions, commands, hooks, resources, artifacts, scalar metrics, and programmatic execution.
@ex.main, @ex.automain, ex.run, and ex.run_commandline.Run objects.interactive=True experiment.configuration-and-cli sub-skill.observers-and-logging sub-skill._seed/_rnd, capture modes, stdout filtering, and reproducibility policy, use the reproducibility-and-capture sub-skill.@ex.main, @ex.automain, @ex.command, @ing.command, @ex.capture, and @ing.capture function as a captured function whose missing parameters may be filled from configuration by name.@ex.main plus explicit ex.run(...) for tests, notebooks, libraries, and multi-run orchestration. Use @ex.automain only in executable scripts and place the decorated function at the end of the file.Experiment(..., interactive=True) with an explicit name for notebook/REPL-style code; otherwise Sacred protects reproducibility by rejecting interactive definitions._run, _config, _log) rather than globals when possible. Do not mutate _config or nested config containers.open_resource, add_resource, add_artifact, and log_scalar only during an active run, either from the Experiment object or the captured _run object.