crw-research

name	crw-research
description	Find ALL the arXiv papers that answer a research question, using fastCRW's Firecrawl-compatible Research API. Use when the ask is to survey a literature, enumerate papers on a topic, find what a paper compares against or builds on, list the best models on a benchmark, or recover a paper from a vague description — "papers that do X", "what does X benchmark against", "best open model on Y", "find the paper that ...". Reaches 61.0% recall on the ArXivQA benchmark vs Firecrawl's Research Index 53.3%.
license	AGPL-3.0
metadata	{"author":"us","version":"0.1.0","homepage":"https://fastcrw.com","repository":"https://github.com/us/crw"}
allowed-tools	Bash(curl:*) Bash(jq:*) Read

fastCRW Research

Find EVERY arXiv paper that answers a research query. Recall = union of arXiv ids; extra ids never hurt, so cast wide but on-topic. The Research API is a live, drop-in Firecrawl-research-compatible surface — your job is query strategy + intent routing, the endpoints do the retrieval.

This skill reaches 61.0% recall on the 191-question ArXivQA benchmark, a number fastCRW measured directly. Firecrawl publishes 53.3% for its own Research Index on the same benchmark in their Research Index launch post (2026-06-17): "On arXivQA, the index hits 53.3% recall at $0.32 per task, against 45.4% for the next best provider."

Set FASTCRW_API_KEY (a crw_live_… key from https://fastcrw.com/dashboard). Base URL https://api.fastcrw.com. Every endpoint is a GET; pull arXiv ids out of results[].ids.arxiv / results[].primaryId.

# search: ranked papers for one query
curl -s -H "Authorization: Bearer $FASTCRW_API_KEY" \
  "https://api.fastcrw.com/v1/search/research/papers?query=$(jq -rn --arg q "QUERY" '$q|@uri')&k=40"

# references / citers / similar of a seed paper (citation graph)
curl -s -H "Authorization: Bearer $FASTCRW_API_KEY" \
  "https://api.fastcrw.com/v1/search/research/papers/arxiv:1706.03762/similar?intent=related%20work&mode=references&k=40"

The whole game: classify the query, apply the matching method

A) ALWAYS (base): write 8–12 exact-name queries — specific method, model, dataset, and benchmark NAMES, not broad phrases ("MoleculeNet benchmark", "Uni-Mol", "ChemBERTa", not "molecular embeddings"). Call search on each, union the arXiv ids, rank by how many queries surfaced each id. Exact-name decomposition is the #1 recall lever — one broad query misses the niche papers.

B) COMPARE-AGAINST ("what does X compare to / build on / baseline against") → resolve X to its arXiv id, then /papers/arxiv:<X>/similar?mode=references. The answer lives in X's own bibliography.

C) USING / EXTENDING X ("models that USE/adopt X") → /similar?mode=citers (forward citations) + exact-name searches for known adopters.

D) BEST-ON-BENCHMARK ("which models score best on X", "largest open model") → search the leaderboard, read the OPEN model names (DeepSeek/Qwen/GLM/Kimi/MiniMax/Llama/Mistral/Gemma — ignore Claude/GPT/Gemini, no papers), then search "<model family> technical report" for each.

E) NICHE ENUMERATION ("papers that do X") → exact-name queries (A) are primary. A tight survey or awesome-list, when on-topic, adds its ids.

Rules

• Recent ids (25xx / 26xx) are REAL — keep them, never discard as "future-dated".
• A query that sounds specific usually still has a family of papers — surface the family, don't stop at one. Only a query naming a paper by title is single.
• Merge ALL ids from every step; method-targeted (references/leaderboard) and exact-name hits first, broad-search tail after. Never invent ids.