kdense-pytorch-lightning

Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

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Repository: eightmm/codex-science
Last source activity: July 10, 2026 at 05:18
Detected SKILL.md language: English
Stars: 2
Forks: 0

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SKILL.md

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Kdense Pytorch Lightning

Use this Codex adapter for the pinned upstream scientific skill.

Catalog status

Status: inactive

Source: kdense

Audit reasons: executable-content

Upstream license: Apache-2.0 license

Inventory: ../../inventory.json

Upstream instructions: ../../../vendor/scientific-agent-skills/skills/pytorch-lightning/SKILL.md

Procedure

Resolve the plugin root as three directories above this skill directory.

Read the matching inventory record and verify its status, reasons, source path, and pinned revision.

Do not read the upstream instructions until the user has seen every audit reason and given explicit acknowledgement. Acknowledgement permits instruction inspection only.

Treat upstream frontmatter as source metadata, not as Codex configuration. Follow the instruction body only where it is compatible with available Codex tools and the current user-approved plan.

Treat tools named for another agent runtime as capability requests. Map them to an available Codex tool only when the behavior is equivalent; otherwise report the unavailable capability.

Ask before credentials, package installation, new network hosts, remote compute, writes, paid services, destructive actions, or imported executable code.

Use $science-provenance for produced scientific outputs and $science-review before presenting scientific claims.

Boundaries

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name	kdense-pytorch-lightning
description	Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.