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writeup
Generate a complete LaTeX research paper from experiment results — including citation gathering, figure descriptions, and iterative refinement.
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。
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Generate a complete LaTeX research paper from experiment results — including citation gathering, figure descriptions, and iterative refinement.
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
Run the complete 4-stage Best-First Tree Search experiment pipeline — from initial implementation through ablation studies, with multi-agent parallel exploration.
Execute a single Best-First Tree Search iteration — select parent node, generate experiment code, execute it, parse metrics, analyze plots, and update the journal.
Run the complete AI Scientist pipeline — from research ideation through experiment execution, paper writing, and peer review. Orchestrates all sub-skills.
Interactively guide the user to create a workshop/topic description file for the AI Scientist pipeline. Asks structured questions and generates a ready-to-use .md file.
Generate novel research ideas with literature search and novelty checking. Produces structured JSON ideas matching the AI Scientist schema.
Search academic literature using Semantic Scholar API or WebSearch. Returns papers with titles, abstracts, citation counts, and optionally bibtex entries.
| name | writeup |
| description | Generate a complete LaTeX research paper from experiment results — including citation gathering, figure descriptions, and iterative refinement. |
You are writing a complete research paper based on experiment results.
--exp-dir <path>: Experiment directory (required)--type <icbinb|icml>: Paper template type — icbinb (4-page workshop) or icml (8-page, default: icbinb)--cite-rounds <N>: Number of citation gathering rounds (default: 5)--reflections <N>: Number of writeup reflection rounds (default: 3)Parse from the user's message.
Read the experiment artifacts:
# Idea description
cat <exp_dir>/idea.md
# Experiment state
python3 -c "
import json, sys
sys.path.insert(0, '.')
from tools.state_manager import load_experiment_state
state = load_experiment_state('<exp_dir>')
print(json.dumps(state, indent=2))
"
Read the best experiment code from the final completed stage. Read stage summaries and journal data for all stages to understand the full experimental narrative.
python3 tools/latex_compiler.py setup <exp_dir>/latex --type <icbinb|icml>
Create empty references file:
touch <exp_dir>/latex/references.bib
Copy figures into the LaTeX directory (template uses \graphicspath{{./figures/}}):
mkdir -p <exp_dir>/latex/figures
cp <exp_dir>/figures/*.png <exp_dir>/figures/*.pdf <exp_dir>/latex/figures/ 2>/dev/null || true
First, check search backend availability:
python3 tools/search.py check
If S2 API is unreachable or rate-limited, use WebSearch exclusively for all citation searches below. Do not waste rounds retrying a broken S2 backend.
For each round:
Identify sections with uncited claims (look for statements without \cite{})
Formulate 2-3 targeted search queries for the needed citations
Search for papers — try S2 first, fall back to WebSearch immediately on failure:
python3 tools/search.py "<citation query>" --limit 5 --json
If this returns no results or exits with error, use WebSearch to search arxiv.org, scholar.google.com, or semanticscholar.org directly. Extract title, authors, year, venue from the search results.
For each relevant paper, add a BibTeX entry to references.bib:
cat >> <exp_dir>/latex/references.bib << 'BIB_EOF'
@article{authorYYYYkeyword,
title={Paper Title},
author={Author Names},
journal={Venue},
year={YYYY},
}
BIB_EOF
Update the paper text with \cite{key} for the added reference.
Validate — every \cite{key} must have a matching references.bib entry. No fabricated references.
Stop early when all major claims are cited (typically 10-20 for a workshop paper, 20-35 for a full paper). Do not force all rounds if citations are sufficient.
Use Claude's native vision capabilities to review each figure.
List all figures in <exp_dir>/figures/:
ls <exp_dir>/figures/*.png <exp_dir>/figures/*.pdf 2>/dev/null
View each figure using the Read tool. For each figure, analyze:
Build a figure description string that will be injected into the paper generation prompt. This ensures the LaTeX generator knows what each figure shows and can reference them accurately.
Write the complete paper content into template.tex. The paper structure should be:
\ref{fig:...})\bibliography{references}Same structure plus:
template.tex between %%%%%%%%%SECTION%%%%%%%%% markers\begin{figure} with \includegraphics{<filename>} for plots (graphicspath handles the directory)\cite{key} for citations matching references.bib keys\mathbb{R}, \mathcal{L}, etc.)python3 tools/latex_compiler.py compile <exp_dir>/latex --main template.tex
Check for errors:
python3 tools/latex_compiler.py pages <exp_dir>/latex/template.pdf
If there are LaTeX errors, read the log file and fix them:
cat <exp_dir>/latex/template.log | grep -A 2 "^!" | head -20
For each reflection round:
If everything looks good, declare "I am done" and exit the loop.
Move the final PDF:
cp <exp_dir>/latex/template.pdf <exp_dir>/paper.pdf
Report:
<exp_dir>/paper.pdf