How do I install the Crw Research skill?

Run npx skillmds@latest add us/crw-research in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Crw Research free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under AGPL-3.

Who published Crw Research?

us (@us) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Crw Research by us — Agent Skill

Name: Crw Research
Author: us

Crw Research

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%.

us 8b4cd89 3.8 KB Updated Sep 17, 2026

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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. Then expand: take the 5 ids the most queries agreed on and pull /papers/arxiv:<id>/similar?mode=references for each, appending any new ids below the union. This second pass is not optional and is not the same as (B): it runs on EVERY query type, and it is where the papers that no query names directly come from.

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.

---name: crw-researchdescription: 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---# fastCRW ResearchFind EVERY arXiv paper that answers a research query. Recall = union of arXivids; extra ids never hurt, so cast wide but on-topic. The[Research API](https://docs.fastcrw.com/research-api) is a live, drop-inFirecrawl-research-compatible surface — your job is **query strategy + intentrouting**, the endpoints do the retrieval.This skill reaches 61.0% recall on the 191-question ArXivQA benchmark, a numberfastCRW measured directly. Firecrawl publishes 53.3% for its own Research Indexon the same benchmark in their [Research Index launchpost](https://www.firecrawl.dev/blog/research-index-launch) (2026-06-17): "OnarXivQA, the index hits 53.3% recall at $0.32 per task, against 45.4% for thenext 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 outof `results[].ids.arxiv` / `results[].primaryId`.```bash# search: ranked papers for one querycurl -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-namedecomposition is the #1 recall lever — one broad query misses the niche papers.Then **expand**: take the 5 ids the most queries agreed on and pull`/papers/arxiv:<id>/similar?mode=references` for each, appending any new idsbelow the union. This second pass is not optional and is not the same as (B):it runs on EVERY query type, and it is where the papers that no query namesdirectly come from.**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 knownadopters.**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) areprimary. 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.

Frequently asked questions