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Firecrawl Research Papers

Organization
firecrawl
firecrawl-research-papers

Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, and public-health topics. Prefer this over a general web-research workflow whenever the evidence base is published papers rather than web pages.

Overview

Publisherfirecrawl
Repositoryfirecrawl-workflows
Skill namefirecrawl-research-papers
Stars
175
Forks
38
Bundled files
Instructions only
LicenseISC
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by firecrawl on GitHub. Read the source before you install it.

Installation

Install the Firecrawl Research Papers AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/firecrawl/firecrawl-workflows/tree/main/skills/firecrawl-research-papers
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/firecrawl/firecrawl-workflows.git /tmp/firecrawl-workflows
mkdir -p .claude/skills
cp -r /tmp/firecrawl-workflows/skills/firecrawl-research-papers .claude/skills/firecrawl-research-papers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Firecrawl Research Papers in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Firecrawl Research Papers on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Firecrawl Research Papers is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Firecrawl Research Papers

Use this to create a sourced literature review.

Onboarding Interview

Infer the topic, source constraints, target count, and output format from context. If the topic is clear, proceed immediately.

Ask at most 1-3 concise questions only if blocked, such as the topic, target paper count, or required venue/date/method constraints.

Firecrawl Collection Plan

Use Firecrawl Research through the CLI, MCP, or equivalent Firecrawl tool surface as the primary path for paper discovery and verification. Fall back to general Firecrawl search and scrape for whitepapers, technical reports, research blogs, leaderboards, or facts outside the paper corpus.

What the paper index holds: paper abstracts, with full text reachable per paper. Its largest share is biomedical and life-science literature — PubMed journal articles plus bioRxiv and medRxiv preprints — so clinical, drug, gene, disease, epidemiology, and public-health questions are in scope. arXiv preprints cover computer science, physics, and mathematics. Coverage outside those sources is thinner, and the web tools below are the fallback there.

Core tools:

  • MCP: firecrawl_research_search_papers(query, k?) CLI: firecrawl research search-papers <query> [--k <number>] Semantic search over paper abstracts. Start here for most paper-finding queries, and retry with alternate framing when results are thin or too narrow.
  • MCP: firecrawl_research_related_papers(seed_ids, intent, mode?, k?) CLI: firecrawl research related-papers <seedIds...> --intent <intent> [--mode <similar|citers|references>] [--k <number>] Expand from strong seed papers into similar work, citing papers, or references. Use this to find the relevant paper family, not just the first matching result.
  • MCP: firecrawl_research_inspect_paper(id) CLI: firecrawl research inspect-paper <id> Fetch canonical metadata for a candidate paper: title, abstract, authors, categories, source ids, and dates.
  • MCP: firecrawl_research_read_paper(id, question) CLI: firecrawl research read-paper <id> --question <question> Verify a specific claim or constraint inside one paper, such as method, reported score, benchmark, affiliation, comparison, or limitation.
  • MCP: firecrawl_search(query) / firecrawl_scrape(url) CLI: firecrawl search <query> / firecrawl scrape <url> Use for web-only context: benchmark leaderboards, rankings, reports, whitepapers, research blogs, and source pages outside the paper index.

Not the paper index, despite the name: passing categories: ["research"] to firecrawl_search (CLI firecrawl search <query> --categories research) filters an ordinary web search to research-affiliated websites — the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites — and returns page results from them. It reaches those sites' web pages; what it does not do is query their paper records in the index above, so there is no abstract search, no related-paper or citation-graph expansion, no canonical paper metadata, and no in-body passages. Use it when a web search is what you want and those sites should be weighted in the same call; use the firecrawl_research_* tools for paper work.

Match the approach to the query:

  • Single named paper: run one paper search, then inspect or read the paper if metadata or body verification is needed.
  • Paper by description, method, or topic family: search for strong anchors, then expand with related papers and keep close neighbors.
  • Enumeration queries, such as papers that do a task or benchmark a method: search multiple framings, expand several strong anchors, and re-seed from newly found relevant papers.
  • Papers that use or exhibit a property: start from the defining paper or strongest anchor, expand via similar, citers, or references, and use read-paper to verify the property.
  • Superlatives and leaderboards: use general web search or scrape to find the ranking, then map top entries back to papers with paper search.
  • Author, organization, venue, date, or methodology constraints: verify with inspect-paper metadata or read-paper before keeping a candidate.

Target source types:

  • biomedical and life-science literature from PubMed, with bioRxiv and medRxiv preprints for work that has not appeared in a journal yet
  • arXiv preprints in computer science, physics, and mathematics
  • academic papers from university sites and ACM/IEEE pages where accessible
  • industry reports and whitepapers
  • company research blogs
  • technical articles and conference summaries

Principles:

  • When in doubt, include the relevant paper family rather than only the single best result.
  • Use related-paper expansion to avoid stopping at one strong hit.
  • Use read-paper to verify load-bearing constraints, not to summarize every candidate.
  • Drop only clearly off-topic papers.

Parallel Work

If appropriate, use sub-agents or equivalent parallel task runners:

  • Academic Papers researcher
  • Biomedical and Life Sciences researcher, for PubMed journal articles and bioRxiv/medRxiv preprints on a clinical, drug, gene, disease, epidemiology, or public-health topic
  • Industry Reports researcher
  • Technical Articles researcher
  • Synthesis and citation reviewer

Split by source or sub-topic, not by tool. Give each researcher the same paper tools and let the topic decide which part of the corpus answers.

Final Deliverable

markdown
# Literature Review: [Topic]

## Abstract
[2-3 paragraph summary]

## Key Papers
[Title, authors, source URL, key findings, methodology, relevance]

## Themes And Consensus
[What sources agree on]

## Open Questions And Debates
[Disagreements and unresolved questions]

## Emerging Trends
[Recent developments]

## Sources
[Organized by paper/report/article]

## Rerun Inputs
workflow: firecrawl-research-papers
topic: [topic]
target_count: [number]
output: [markdown/brief]

Quality Bar

  • Every major claim should trace to a source.
  • Note inaccessible or failed PDFs.
  • Distinguish peer-reviewed work from blogs and vendor reports.

Frequently asked questions

What does the Firecrawl Research Papers AI skill do?

Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, a...

Why use Firecrawl Research Papers on TypingMind?

Because you install it once and use it with any model. Firecrawl Research Papers is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Firecrawl Research Papers in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/firecrawl/firecrawl-workflows/tree/main/skills/firecrawl-research-papers. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Firecrawl Research Papers?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Firecrawl Research Papers?

As many as you like. As long as a model supports skills, you can use Firecrawl Research Papers with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Firecrawl Research Papers AI skill free?

Yes. It is published on GitHub by firecrawl under the ISC license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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