菜单
Create a structured reader for RL, MARL, LLM, and LLM Agent papers. Use for paper summaries, Chinese-English reading notes, method decomposition, experiment extraction, reproducibility analysis, and idea mining.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Search, plan, and organize literature for RL, MARL, LLM, and LLM Agent research. Use for finding papers, building related-work maps, identifying baselines, benchmark provenance, citation gaps, and recent work tracking.
Build, audit, and organize citation evidence for RL, MARL, LLM, and LLM Agent manuscripts. Use for related work matrices, citation gaps, DOI/BibTeX checks, baseline provenance, benchmark provenance, and Nature-style support for claims.
Prepare Data Availability, Code Availability, Model Availability, Prompt Availability, and reproducibility statements for RL, MARL, LLM, and LLM Agent manuscripts.
Help legally obtain, organize, and archive academic papers, PDFs, BibTeX, supplementary files, code links, and benchmark documents for RL, MARL, LLM, and LLM Agent research. Do not bypass paywalls or access controls.
Create, revise, or audit Nature-style scientific figures for RL, MARL, LLM, and LLM Agent papers. Use for Python/R plotting, training curves, ablations, benchmark bars, architecture diagrams, agent workflow figures, trajectory visualizations, and publication export QA.
Build a repeatable literature discovery, scoring, reading, and archiving pipeline for RL, MARL, LLM, and LLM Agent research. Use for daily/weekly paper tracking, topic watchlists, benchmark monitoring, and research-note automation.
| name | nature-reader |
| description | Create a structured reader for RL, MARL, LLM, and LLM Agent papers. Use for paper summaries, Chinese-English reading notes, method decomposition, experiment extraction, reproducibility analysis, and idea mining. |
| version | 2.0.0-agent-rl |
Do not only summarize. Extract what a researcher needs to reuse, critique or extend the paper.
Paper card
- Problem:
- Core idea:
- Contribution type:
- Method in one paragraph:
- Key equations/algorithms:
- Experiments:
- Strongest evidence:
- Weakest evidence:
- Reproducibility checklist:
- Ideas I can borrow:
- Risks if citing this paper:
Do not claim to have read figures, tables or appendices unless they were provided or accessible. When information is missing, label it as missing.