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ml-paper-writing
Use when drafting ML/AI papers, verifying citations, framing evidence, using LaTeX, or preparing submissions.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
메뉴
Use when drafting ML/AI papers, verifying citations, framing evidence, using LaTeX, or preparing submissions.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use when copy editing, proofreading, polishing, or removing AI-sounding prose.
Use when creating Data/AI strategy, principles, roadmaps, MLOps plans, or executive docs.
Use when coaching technical leaders on conflict, burnout, cofounders, CTO transitions, or growth.
Use when writing Manning-style technical chapters, Chapter 1s, examples, callouts, or summaries.
Use when writing strategic tech posts, engineering narratives, opinion pieces, or industry analysis.
| name | ml-paper-writing |
| description | Use when drafting ML/AI papers, verifying citations, framing evidence, using LaTeX, or preparing submissions. |
| allowed-tools | Read,Write,Edit,Bash,Glob,Grep,WebSearch,WebFetch |
Use for ML/AI research papers targeting venues such as NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. Be proactive when the repo, results, and contribution are clear.
Never invent citations or BibTeX. Search, verify, fetch BibTeX from DOI when possible, and mark unresolved references as placeholders.
.bib, and experiment outputs.The introduction must make the contribution legible before methods. Every experiment states the claim it tests. Baselines must be fair. Figures should be self-contained, readable, and colorblind-safe. Claim strength must match evidence.
Load citation, structure, evidence, writing, style, formatting, checklist, abstract, or reviewer references only when the current paper task needs them.