Draft a LaTeX research paper from all previous stage outputs
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Take all previous outputs (ideation, planning, experiment results) and produce a complete LaTeX paper draft with figures, tables, and bibliography.
generate_latex: Generate and assemble LaTeX source files for each paper sectioncompile_pdf: Compile the LaTeX source into a PDF documentgenerate_figure: Produce publication-quality figures from experiment resultsideation_output: Path to papers/ideation_output.json from the ideation skillexperiment_blueprint: Path to papers/experiment_blueprint.json from the planning skillexperiment_results: Path to experiments/ directory containing code and results from the experiment skillgenerate_figuregenerate_latexcompile_pdf and verify the outputProduces papers/draft/ directory containing:
main.tex: Complete LaTeX source of the paperreferences.bib: Bibliography file with all citationsfigures/: Generated figures in PDF or PNG formattables/: LaTeX table source filesmain.pdf: Compiled PDF of the paper draftまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
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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.
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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.
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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.
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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.
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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.
日本語の概要は準備中です。原文の説明を表示しています。