소스 정보
- 저장소
- taracodlabs/aiden
- 최근 소스 활동
- 2026년 5월 6일 12:31
- 감지된 SKILL.md 언어
- 영어
- 스타
- 779
- 포크
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설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/taracodlabs/aiden --skill research-paper-writing명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | research-paper-writing |
| description | Pipeline for ML/AI research papers — lit review to LaTeX submission |
| category | research |
| version | 1.0.0 |
| origin | aiden |
| license | Apache-2.0 |
| tags | research, paper, writing, latex, ml, ai, academic, arxiv, publication, citation |
A structured, step-by-step pipeline for writing ML/AI research papers: from problem framing through literature review, experiment design, writing, and LaTeX formatting for arXiv submission.
Define the paper's core claim before writing anything else.
1. One-sentence contribution: "We show that X outperforms Y on Z by doing W"
2. Key insight: what is non-obvious about your approach?
3. Research question: what question does the paper answer?
4. Limitations scope: what is explicitly out of scope?
Use the arXiv skill to find related work, then organize findings:
Search strategy:
- Start with 2-3 "seed" papers you know are relevant
- Find papers that cite them (via Semantic Scholar API)
- Search arXiv for your core keywords + recent date filter
- Organize into: direct predecessors, concurrent work, tangential work
For each related paper, note:
- Core method
- Dataset/benchmark used
- Key result number
- How your work differs
# Semantic Scholar API — find papers citing a known paper
import requests
paper_id = "arXiv:2305.17333"
resp = requests.get(f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}/citations?fields=title,year,authors,externalIds&limit=20")
for c in resp.json()["data"]:
print(c["citingPaper"]["title"])
Standard ML/AI paper structure:
Abstract (150-250 words) — problem, method, key result, significance
1. Introduction — motivation, gap, contribution, paper overview
2. Related Work — organize by theme, not chronologically
3. Method — notation, architecture/algorithm, key design choices
4. Experiments — datasets, baselines, metrics, implementation details
5. Results — main table, ablation study, qualitative examples
6. Discussion — limitations, failure modes, future work
7. Conclusion — restate contribution, broader impact
References
Appendix (optional) — proofs, additional experiments, hyperparameters
Basic arXiv-ready template:
\documentclass[10pt,twocolumn]{article}
\usepackage{arxiv} % from https://github.com/kourgeorge/arxiv-style
\usepackage{amsmath,amssymb,graphicx,booktabs,hyperref}
\title{Your Paper Title}
\author{Author One \and Author Two}
\date{\today}
\begin{document}
\maketitle
\begin{abstract}
Your abstract here. State the problem, method, key result, and significance in 150--250 words.
\end{abstract}
\section{Introduction}
...
\bibliography{refs}
\bibliographystyle{plain}
\end{document}
% Results table with booktabs
\begin{table}[t]
\centering
\caption{Comparison on benchmark dataset.}
\begin{tabular}{lcc}
\toprule
Method & Accuracy & F1 \\
\midrule
Baseline & 72.3 & 71.1 \\
Prior SOTA & 78.6 & 77.9 \\
\textbf{Ours} & \textbf{83.2} & \textbf{82.7} \\
\bottomrule
\end{tabular}
\label{tab:results}
\end{table}
# Compile LaTeX (requires MiKTeX or TeX Live)
pdflatex paper.tex
bibtex paper
pdflatex paper.tex
pdflatex paper.tex # run twice to resolve references
# Check word count
texcount paper.tex
"Help me write the abstract for my paper on efficient transformers" → Use Phase 1 to extract the core claim, then write 4 sentences: problem → gap → method → key result.
"I need to find related papers on sparse attention before writing the related work section"
→ Use Phase 2: search arXiv (cs.LG + sparse attention), use Semantic Scholar to find citing papers.
"Format my experiment results as a LaTeX table" → Use Phase 5 with the booktabs template.
pdflatex before submitting