hf-papers

name	hf-papers
description	Discover ML research papers, read their contents by section, trace citations, find linked datasets/models — combining HuggingFace Papers, arXiv, and Semantic Scholar. Use whenever you need to research a method, find training recipes with published results, crawl a citation graph, or locate the datasets a paper used. This is your starting point for any literature-first workflow.

hf-papers

Backed by scripts/papers.py. All subcommands call that script via Bash.

When to use

• You need current literature on a task (your internal knowledge is outdated).
• You have an anchor paper and need to crawl its citation graph.
• You want the methodology section of a specific paper — full text, not abstract.
• You need to find datasets/models linked to a paper.
• You need to check if there's a more recent paper that beat a known result.

Operations (via --op)

Operation	What it does	Key flags
trending	Trending papers on HF daily, optionally filtered by keyword	--query, --date YYYY-MM-DD
search	Search papers (HF by default; Semantic Scholar when filters set)	--query, --date-from, --date-to, --min-citations, --categories, --sort-by
paper_details	Metadata, abstract, AI summary, GitHub link, citation counts	--arxiv-id
read_paper	Abstract + TOC (no --section), or full section text	--arxiv-id, --section
citation_graph	References + citations with isInfluential flag and intents	--arxiv-id, --direction
snippet_search	Semantic search across 12M+ full-text passages	--query, --min-citations
recommend	Similar papers (single seed or positive/negative lists)	--arxiv-id OR --positive-ids, --negative-ids
find_datasets	HF datasets linked to a paper	--arxiv-id, --sort
find_models	HF models linked to a paper	--arxiv-id, --sort
find_collections	HF collections containing a paper	--arxiv-id
find_all_resources	Datasets + models + collections in parallel	--arxiv-id

All ops accept --limit N (default 10, max 50) and --json for a minimal JSON envelope.

Canonical literature-crawl flow

# 1. Find the anchor paper
python scripts/papers.py --op search --query "GPQA graduate questions" --sort-by citationCount

# 2. Crawl citations downstream — who improved on it?
python scripts/papers.py --op citation_graph --arxiv-id 2311.12022 --direction citations

# 3. Read methodology of a promising downstream paper
python scripts/papers.py --op read_paper --arxiv-id 2404.01348                 # TOC
python scripts/papers.py --op read_paper --arxiv-id 2404.01348 --section 3     # Methodology
python scripts/papers.py --op read_paper --arxiv-id 2404.01348 --section 4     # Experiments

# 4. Find resources linked to it
python scripts/papers.py --op find_all_resources --arxiv-id 2404.01348

# 5. Inspect the dataset they used
python scripts/hf_docs.py explore --endpoint trl --query sft    # pivot to code once recipe is clear

Environment

• S2_API_KEY (optional) — raises Semantic Scholar rate limits.

Output contract

Every op prints markdown-formatted text to stdout. Exit code 0 on success, 1 on error. With --json you get {"op": "...", "output": "..."}.

Related skills

• research-subagent — when the crawl is heavy, spawn a sub-agent and pass this skill's invocation pattern as its toolkit. Keeps the main context clean.
• hf-datasets — call right after find_datasets to verify the top result.
• github-code — pivot to here once you've extracted a training recipe, to find working implementation code.