Best AI Agent Skills for Research Papers (2026)

A PhD student I talked to spends roughly a day a week on the mechanical parts of research: tracking down papers, pulling quotes out of PDFs, formatting citations, reformatting the manuscript for a journal's template. None of that is the research. It's the friction around the research. Claude Code skills can take most of that day back.

A skill is a folder with a SKILL.md that teaches Claude a defined task plus any scripts it needs. For academic work, four skills cover the parts that eat your time without touching the part that needs your brain, the thinking. Here's each one, how to install it, and the catch.

deep-research: the literature sweep, faster

The deep-research skill runs structured, multi-source searches and brings back findings with their sources attached. For a literature review, it surfaces relevant work and summarizes the state of a question in a fraction of the time a manual search takes. It's strongest as a starting map: it tells you where the conversation is happening so you know what to read closely.

git clone https://github.com/anthropics/skills ~/.claude/skills-temp
cp -r ~/.claude/skills-temp/deep-research ~/.claude/skills/deep-research

The non-negotiable caveat: an AI research tool can misattribute a claim or surface a paper that doesn't say what the summary implies. Citation errors in academic work are not a small embarrassment, they undermine the whole piece. Use the skill to find sources, then read the actual papers and confirm each claim before it goes near your draft. Never cite a paper you haven't opened.

pdf-tools: get the content out of the paper

Research lives in PDFs, and PDFs are hostile to copy-paste. The pdf skill extracts text, tables, and figures cleanly, so you can pull a methods section, a results table, or a specific quote without retyping it. It's the workhorse you'll use most.

cp -r ~/.claude/skills-temp/document-skills/pdf ~/.claude/skills/pdf-tools

Scanned PDFs are the weak point. Older papers and some archives are images, not text, and OCR introduces errors, especially in equations and tables. For anything you'll quote or report exactly, check the extraction against the original page.

firecrawl-skill: sources that aren't in a database

Not every source is a journal article. Government datasets, institutional reports, archived pages, and primary sources often live on the open web in formats no academic database indexes. The firecrawl skill scrapes those pages and returns structured content you can work with.

cp -r ~/.claude/skills-temp/firecrawl ~/.claude/skills/firecrawl-skill

Two cautions. First, respect each site's terms of service and rate limits, scraping aggressively can get you blocked or worse. Second, web sources need the same scrutiny as any other: a scraped page is only as reliable as whoever published it. The skill gets you the content; judging its credibility is still your job.

docx-word: the manuscript, formatted to spec

Writing the paper is yours. Formatting it to a journal's exact template is where the docx skill helps. It drafts and formats Word documents, handles headings and structure, and can reformat a manuscript to match a target template, which saves the soul-draining hours before a submission deadline.

cp -r ~/.claude/skills-temp/document-skills/docx ~/.claude/skills/docx-word

The check here is compliance. Journals are picky about margins, heading styles, reference format, and figure placement, and a generated document can miss a rule. Run it against the journal's checklist before you submit.

How the four work together

A realistic flow for a literature review:

1. deep-research maps the field and surfaces candidate papers with sources. 2. pdf-tools extracts the methods, results, and quotes from the papers you select. 3. firecrawl-skill pulls in any web-only sources, like a dataset or an institutional report. 4. docx-word drafts and formats the writeup to your target template. 5. You read every source, verify every claim, and write the analysis.

The skills handle searching, extracting, scraping, and formatting. The argument, the synthesis, the original contribution, that's the part no skill can do, and the part that's actually your work. For the standalone apps that complement these skills, our full list for researchers covers the broader toolkit.

The line you don't cross: integrity

Every skill here speeds up work around your research. None of them should write the research. The distinction matters more in academia than almost anywhere, because the rules are explicit and the penalties are career-shaped. Using a skill to extract a table from a PDF is data processing. Using it to generate an analysis you present as your own thinking is misconduct at most institutions, and increasingly journals require disclosure of AI use in the methods or acknowledgments.

The safe frame: AI handles the mechanical labor, you produce every interpretive claim, and you disclose what you used. Check your institution's policy and your target journal's AI statement requirements before you start, not after a reviewer asks. The tools that save you a day a week are worth nothing if they cost you the paper.

A note on reproducibility

One underrated use of these skills is making your own work easier to reproduce. If you build your literature search, extraction, and formatting as a documented sequence rather than a pile of manual steps, you, or a collaborator, or a reviewer, can rerun it and get the same result. Save the prompts you used with deep-research, keep the extracted data the pdf skill produced alongside the source files, and note which version of each skill you ran. Six months later, when you're revising for resubmission and can't remember where a number came from, that paper trail is the difference between a clean revision and a frantic re-search. Reproducibility is a habit, and these skills make the habit cheaper to keep.

Security: audit before you install

Skills run with your permissions and can execute code and read your files. For academic work, where you may be handling unpublished data or others' confidential drafts, the bar is higher. Before installing any skill:

• Read the SKILL.md and any scripts first. If you can't follow what it does, don't run it.
• Stick to known sources. The official Anthropic skills at github.com/anthropics/skills are a safe baseline. For community skills, check stars, authorship, and how recently it was updated.
• Reject anything that sends data to an unknown endpoint. Your unpublished research has no reason to leave your machine.
• Watch network and shell access. A PDF extractor doesn't need to phone home.

The few minutes an audit takes is nothing against the cost of leaking unpublished work or citing a hallucinated source.

Skills versus the dedicated research apps

There's a wave of AI tools built specifically for academics, and they're worth knowing. So where do skills fit? Skills win on three things: they run locally against your own files, you can read exactly what they do, and they chain inside one session so research, extraction, and writing happen without shuttling data between web apps. The dedicated apps often win on polish and purpose-built features like reference managers or citation graphs. The realistic answer for most researchers is both: use a citation manager for what it's built for, and use skills for the extraction, scraping, and formatting glue that no single app handles end to end. The point of the skills isn't to replace your whole stack, it's to remove the manual handoffs between the parts of it.

FAQ

Can AI agent skills write my research paper for me? No, and you shouldn't want them to. They handle the mechanical parts: literature searching, PDF extraction, web scraping, and formatting. The argument, synthesis, and original contribution are yours. Using AI to write the analysis risks both academic integrity violations and shallow work.

Which skill is best for a literature review? The deep-research skill, which runs multi-source searches and returns findings with citations, so you get a fast map of the field. Always read the actual papers it surfaces and verify each claim before citing, since AI research tools can misattribute sources.

Are these skills safe for unpublished or confidential research data? Only after you audit them. Read the SKILL.md and scripts, install from known sources like the official Anthropic repo, and reject anything that transmits data externally. Skills run with your permissions, so treat them like running someone else's code on your machine.

How do I install a Claude Code skill for academic work? Clone the official skills repo from github.com/anthropics/skills, then copy the skill folder you want into ~/.claude/skills/ and restart Claude Code. It loads the skill automatically when your task matches its description.

Can these skills handle scanned PDFs of old papers? The pdf skill extracts text from scanned documents via OCR, but OCR makes errors, especially on equations, tables, and older typefaces. For anything you'll quote or report precisely, check the extracted text against the original page.