菜单
Polish, rewrite, translate, or tighten academic prose for RL, MARL, LLM, and LLM Agent manuscripts. Use for Nature-style English, SCI writing, paragraph flow, terminology consistency, LaTeX expression, and claim-risk reduction.
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-polishing |
| description | Polish, rewrite, translate, or tighten academic prose for RL, MARL, LLM, and LLM Agent manuscripts. Use for Nature-style English, SCI writing, paragraph flow, terminology consistency, LaTeX expression, and claim-risk reduction. |
| version | 2.0.0-agent-rl |
Polishing must improve scientific precision, not merely make prose sound elegant. Preserve technical meaning and reduce unsupported claims.
policy, value function, trajectory, return, sample efficiency, offline RL;centralized training with decentralized execution, ad hoc teamwork, zero-shot coordination, mixed-motive game;planner, tool schema, memory, trajectory, agent environment, prompt, retrieval corpus.For complete rewrites, return the polished text first. Then provide:
Major changes:
- clarity:
- technical precision:
- claim control:
- grammar/style:
Do not add nonexistent experimental results, citations, tasks or theoretical guarantees. Do not turn a weak result into a SOTA claim. Do not erase important limitations.