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。
, nested ids, and `ConfigParser`.\n- Read `references/cli-reference.md` when invoking `python -m monai.bundle`, selecting commands, preparing CLI overrides, validating metadata/network I/O, exporting checkpoints, downloading large files, or pushing bundles.\n- Read `references/troubleshooting.md` when Bundle parsing, CLI startup, verification, export, path, optional dependency, or network/download behavior fails.\n- Run `scripts/monai_bundle_smoke.py --help` when checking what the bundled smoke script can do without touching external data or repository source files.\n- Run `scripts/monai_bundle_smoke.py parse-inline` when confirming the installed MONAI Bundle parser can instantiate a tiny object graph and apply an override.\n- Run `scripts/monai_bundle_smoke.py build-tiny-bundle --output \u003cdir>` when a temporary self-contained Bundle directory is useful for experimentation.\n- Use `templates/tiny-bundle/` as a copyable minimal Bundle skeleton when a user asks for example bundle files rather than a generated temporary directory.\n\n## Common Workflows\n\n1. For config authoring, start with `references/config-syntax.md`, keep top-level ids meaningful, use `_target_` for instantiable Python objects, and prefer simple `@` references over complex `
bundle-config【数据与研究】Work with MONAI Bundle configuration syntax, metadata/spec files, ConfigParser, ConfigWorkflow, Bundle CLI commands, verification, export, and package-style bundle workflows.
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Use this sub-skill when the task involves monai.bundle, python -m monai.bundle, Bundle JSON/YAML config syntax, metadata/spec files, ConfigParser, BundleWorkflow, ConfigWorkflow, CLI overrides, verification, export, or reproducible package-style bundle workflows.
Do not use this sub-skill for Auto3DSeg orchestration that generates bundles; route those tasks to apps-auto3dseg. For Bundle component internals, route model definitions to the model sub-skill, transforms and datasets to the data/transforms sub-skills, and training loop objects to the training sub-skill.
references/config-syntax.md when authoring, merging, overriding, or debugging Bundle JSON/YAML with _target_, @, %, $, nested ids, and ConfigParser.references/cli-reference.md when invoking python -m monai.bundle, selecting commands, preparing CLI overrides, validating metadata/network I/O, exporting checkpoints, downloading large files, or pushing bundles.references/troubleshooting.md when Bundle parsing, CLI startup, verification, export, path, optional dependency, or network/download behavior fails.scripts/monai_bundle_smoke.py --help when checking what the bundled smoke script can do without touching external data or repository source files.scripts/monai_bundle_smoke.py parse-inline when confirming the installed MONAI Bundle parser can instantiate a tiny object graph and apply an override.scripts/monai_bundle_smoke.py build-tiny-bundle --output <dir> when a temporary self-contained Bundle directory is useful for experimentation.templates/tiny-bundle/ as a copyable minimal Bundle skeleton when a user asks for example bundle files rather than a generated temporary directory.references/config-syntax.md, keep top-level ids meaningful, use _target_ for instantiable Python objects, and prefer simple @ references over complex $ expressions.python -m monai.bundle <command> -- --help, and quote shell-sensitive ids such as network_def#spatial_dims.verify_metadata for schema-backed metadata and verify_net_in_out only when the config network can run safely on the requested device with fake tensor input.ckpt_export, onnx_export, or trt_export.run_workflow/create_workflow with explicit config_file, meta_file, workflow_type, and overrides instead of relying on current working directory defaults.<bundle_dir>, <config_file>, and <checkpoint.pt>.monai, torch, and numpy APIs.