Claude Skills by OpenRaiser

Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

Generate a Python code skeleton from an experiment blueprint

Search academic literature and generate research hypotheses

Produce an experiment blueprint from a research hypothesis

Draft a LaTeX research paper from all previous stage outputs

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experi...

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.

Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

本技能专门处理 **Category C：排版一致性缺陷**，包括：

本技能专门处理 **Category B：浮动体缺陷**，包括：

本技能专门处理 **Category D：溢出与对齐缺陷**，包括：

本技能专门处理 **Category A：空间利用缺陷**，包括：

本技能为 **Visual Typesetting Optimization (VTO)** 任务提供完整的视觉排版缺陷分类知识库。它是 `layout-detective-agent` 进行逐页视觉诊断的核心参考手册，定义了每一类缺陷的**视觉特征、诊断方法、严重等级判定**以及与编译日志信号的对应关系。

本技能专门处理 **Category E：跨模板迁移缺陷**，包括：

本技能是 PaperFit 视觉排版优化闭环中的关键环节，专门负责 **PDF 页图渲染与视觉验收指导**。它封装了将 PDF 转换为逐页高分辨率图片的标准化流程，并为 `layout-detective-agent` 和 `quality-gatekeeper-agent` 提供详细的逐页视觉检查清单。

本技能为 **Semantic Polish Agent** 提供具体、可执行的语义微调策略与禁区规则。它定义了在排版手段用尽后，如何通过最小化文字增删来消除孤行寡行、控制页数预算或优化末页留白，同时严格保持学术内容的原意、数据和结论不变。