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
Skills 둘러보기
생산성 및 협업 · SKILL.md
integrations-and-plugins
integrations-and-pluginsRoute SwanLab framework callback adapters, notification plugins, CSV writer, and callback protocol questions.
Independent GitHub snapshot · Automated classification · Original documentation governs
Skill 문서
GitHubname: integrations-and-plugins description: "Route SwanLab framework callback adapters, notification plugins, CSV writer, and callback protocol questions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0
Integrations and Plugins
Use this sub-skill for SwanLab adapter and plugin questions that are not about base experiment tracking, media objects, or settings/credentials.
Route here for
- Framework callback adapters: Transformers, PyTorch Lightning, Keras, LightGBM, XGBoost, CatBoost, Ray, Accelerate, MMEngine, Ultralytics, FastAI, Stable-Baselines3, PaddleNLP, and Torchtune.
- Plugin callbacks: notifications and CSV writer behavior.
- Custom
Callbacksubclasses, callback dedupe, merge order, and lifecycle hooks. - Optional dependency import errors for framework adapters.
Use these references
Route away from here
- Base tracking APIs and run lifecycle → experiment-tracking.
- Media object details and custom charts → media-and-custom-charts.
- Settings, hosts, login, and credential storage → settings-and-modes.
Working rule
Keep the answer focused on the adapter or plugin hook path, then call out the exact callback name, required package, and any single-process or merge-order caveat.