Research

Academic Paper Research & Dataset Discovery

Instructions and tools for searching, fetching, and extracting ML/AI research papers, plus finding and downloading datasets.

Prerequisites

which pdftotext || echo "MISSING: brew install poppler (macOS) or apt install poppler-utils (Linux)"

Available sources (7 total)

Commands

Search

uv run ${CLAUDE_SKILL_DIR}/scripts/search.py "$ARGUMENTS" --source arxiv --max 5

Flags: --source (arxiv, semantic_scholar, papers_with_code, huggingface, jmlr, openscholar), --max N, --cat cs.AI,cs.LG, --sort relevance|date

Fetch metadata

uv run ${CLAUDE_SKILL_DIR}/scripts/fetch.py <paper_id>

Auto-detects source: 2401.12345 (arXiv), 2022.acl-long.220 (ACL), v22/19-920 (JMLR), 40-char hex (Semantic Scholar)

Download PDF

uv run ${CLAUDE_SKILL_DIR}/scripts/download.py <paper_id> -o ./papers/

Extract text

uv run ${CLAUDE_SKILL_DIR}/scripts/extract.py ./papers/<file>.pdf --max-pages 20

Guidelines

• Search 3-5 papers, not 20. Read abstracts first.
• Only download PDFs for papers the user cares about.
• Use --max-pages 10 for summaries; go deeper only when asked.
• Summarize key findings, then offer to dive deeper.
• Always cite: title, authors, year, source URL.

Additional tools

Multi-source concurrent search (alternative)

uv run ${CLAUDE_SKILL_DIR}/scripts/scientific_search.py "$ARGUMENTS" --max 10

Searches arXiv + Semantic Scholar concurrently with deduplication. Add --datasets for Kaggle/HuggingFace datasets.

Document analysis

uv run ${CLAUDE_SKILL_DIR}/scripts/analyze_document.py <url_or_path> [--max-pages 10] [--json]

Extracts text from PDFs, Word docs, text files. Supports URLs and local paths.

Dataset Discovery & Download

Available dataset sources (5 total, all free, no API keys)

Search datasets

uv run ${CLAUDE_SKILL_DIR}/scripts/datasets.py search "$ARGUMENTS" --source huggingface --limit 10

Flags: --source (huggingface, openml, uci, paperswithcode, kaggle), --limit N

Get dataset info

uv run ${CLAUDE_SKILL_DIR}/scripts/datasets.py info <dataset_id> --source huggingface

Shows description, columns, splits, download URLs. Works with: huggingface, openml, uci, paperswithcode.

Download dataset

uv run ${CLAUDE_SKILL_DIR}/scripts/datasets.py download <dataset_id> --source huggingface --output ./datasets --split train

Downloads to ./datasets/ directory. Flags: --output DIR, --split train|test|validation (HuggingFace only).

Dataset guidelines

• Search 3-5 datasets, compare sizes and licenses before downloading.
• Use info to check columns/splits before downloading.
• HuggingFace downloads as Parquet (read with pd.read_parquet()).
• OpenML downloads as ARFF, auto-converts to CSV when possible.
• For Kaggle downloads, use: kaggle datasets download -d <owner/dataset>

Rate limits (enforced automatically)

arXiv: 3s, Semantic Scholar: 4s, Papers with Code: 3s, JMLR: 3s per volume, OpenML: 2s, UCI: 2s, Kaggle: 2s.

Reference

• references/sources.md — API details, endpoints, rate limits for all sources

Paper Review

Structured framework for reviewing ML/AI research papers. Produces fair, constructive, conference-quality reviews.

Obtain the paper

Use the research scripts to get the paper content:

# Download by arXiv ID
uv run ${CLAUDE_SKILL_DIR}/scripts/download.py 2401.12345 --output ./papers

# Extract text from PDF
uv run ${CLAUDE_SKILL_DIR}/scripts/extract.py ./papers/2401.12345.pdf --max-pages 30

# Fetch metadata
uv run ${CLAUDE_SKILL_DIR}/scripts/fetch.py 2401.12345

If the user provides only a topic, search first, then review the selected paper.

Review template

Summary

2-3 sentences: What is the paper about? What is the key contribution?

Strengths

Evaluate each dimension:

• Novelty: Is the approach new? Does it advance the field?
• Experiments: Well-designed? Sufficient baselines?
• Clarity: Well-written? Easy to follow?
• Significance: Would this matter if results hold?

Weaknesses

Identify specific issues:

• Missing baselines or comparisons
• Claims not supported by evidence
• Methodology gaps or questionable choices
• Limited evaluation scope
• Scalability concerns

Methodology assessment

Reproducibility checklist

• Code available (or promised)?
• Dataset available or described sufficiently?
• Hyperparameters fully specified?
• Compute requirements stated (GPU type, hours)?
• Random seeds reported?
• Training details sufficient to reproduce?
• Preprocessing steps documented?

Questions for authors

3-5 specific questions that would strengthen the paper or clarify ambiguities.

Overall assessment

Review ethics

• Be constructive — suggest improvements, don't just criticize
• Separate factual issues from opinions
• Acknowledge uncertainty in your assessment
• Evaluate what was claimed, not what wasn't attempted
• Consider the target venue and its standards
• Give credit where due — acknowledge genuine contributions

Comparative review (multiple papers)

When reviewing multiple papers on the same topic:

Rank by overall contribution, noting complementary strengths.

Prototyping: Paper → Code

Convert a research paper, article, or technical document into a complete working code project.

uv run ${CLAUDE_SKILL_DIR}/scripts/prototype/main.py <source> -o ./prototype [-l python] [-v]

Pipeline

1. Extract — parse PDF, web page, notebook, or markdown
2. Analyze — detect algorithms, architectures, domain, dependencies
3. Select language — explicit flag → code in source → domain default → Python
4. Generate — complete project scaffold with algorithm implementations

Every generated project includes: main implementation, dependency manifest, test file, README, .gitignore.

Language defaults by domain

Code quality requirements

• No TODOs or placeholders — all code must be complete and runnable
• Type hints on all functions (Python, TypeScript)
• Docstrings on all public functions
• Specific error handling (not bare except)
• At least one test file
• README includes: title, install, usage, source attribution

See references/prototype/generation-rules.md for full generation rules.