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Found 188 Skills
Refactor PyTorch code to improve maintainability, readability, and adherence to best practices. Identifies and fixes DRY violations, long functions, deep nesting, SRP violations, and opportunities for modular components. Applies PyTorch 2.x patterns including torch.compile optimization, Automatic Mixed Precision (AMP), optimized DataLoader configuration, modular nn.Module design, gradient checkpointing, CUDA memory management, PyTorch Lightning integration, custom Dataset classes, model factory patterns, weight initialization, and reproducibility patterns.
Controlled plan execution with human review checkpoints - loads plan, executes in batches, pauses for feedback. Supports one-go (autonomous) or batch modes.
Scan an experiment repo and generate a complete paper outline (H1/H2/H3) with user approval checkpoints at each level, then generate body text with evidence annotations, citations, and bilingual output. Python ML repos. 扫描实验仓库，逐级生成论文大纲（H1/H2/H3），每级用户确认后推进， 然后生成带证据标注、引用和双语输出的正文文本。
Process multiple documents in bulk with parallel execution
Exactly-once processing semantics with distributed coordination for file-based data pipelines. Atomic file claiming, status tracking, and automatic retry with in-memory fallback.
Systematically debug issues, investigating bugs, troubleshooting problems, or tracking down errors with persistent state across context resets. Triggers include "debug", "investigate bug", "troubleshoot", "find the problem", "why isn't this working", and "debug session".
Automatic risk assessment before every critical action in agentic workflows. Detects irreversible operations (file deletion, database writes, deployments, payments), classifies risk level, and requires confirmation before proceeding. Triggers on destructive keywords like deploy, delete, send, publish, update database, process payment.
Orchestration pattern for sequential, dependent tasks. When work must flow through stages where each stage depends on the previous (design → implement → test → review), structure as a pipeline with explicit handoffs. Each stage completes before the next begins.
Simplified state file management with consolidated FLOW.md (project) and ITEM-XXX.md (per-item) files
External verl end-to-end validation workflow for Megatron-Bridge model/provider changes. Covers running a small verl Megatron backend job from a Bridge checkout, choosing LoRA/DDP plus optional save/resume and parallelism variants, setting PYTHONPATH so verl imports the local Bridge tree, and reporting pass/fail evidence.
Use when user says "execute epic [id]" or when executing beads epics with parallel subagents in the current session
Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.