Generate a Python code skeleton from an experiment blueprint
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Take the experiment blueprint and produce a runnable Python code skeleton that implements the proposed method, baselines, training loops, evaluation harness, and ablation configurations.
None. This skill operates entirely through LLM code generation based on the experiment blueprint.
experiment_blueprint: Path to papers/experiment_blueprint.json produced by the planning skillProduces experiments/ directory containing:
data/: Data loading and preprocessing modulesmodels/: Model architecture implementations (proposed method and baselines)training/: Training loop and optimization utilitiesevaluation/: Metric computation and result aggregationconfigs/: YAML configuration files for each experiment and ablation variantrun.py: Main entry point for launching experimentsrequirements.txt: Python dependencies还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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.
中文简介正在准备中。当前显示原文说明。
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 experiments, or managing a multi-hypothesis research effort.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。