claude code for researchers

by Ray Svitla

researchers have a complicated relationship with AI tools. they study them, critique them, publish papers about their limitations, and then quietly use them at 11pm to debug their analysis scripts. no judgment. the deadlines are real.

Claude Code for research is different from Claude Code for software development in ways that matter. the stakes are higher (reproducibility), the data is messier (always), and getting something wrong isn’t just a bug — it’s potentially a retraction.

here’s what’s genuinely useful, what’s risky, and where the line is.

literature review acceleration

reading papers is slow. reading the wrong papers is slow and depressing. Claude Code helps with the triage phase — not reading papers for you, but helping you find and organize the ones worth reading.

the workflow: dump a folder of PDFs into your project. ask Claude to extract titles, abstracts, methods sections, and key findings. have it cluster papers by methodology, identify citation networks, and flag papers that multiple others cite as foundational.

what Claude gives you: a structured map of a research area in an hour instead of a week. which groups are working on what. which methods are standard. where the disagreements live.

what Claude can’t give you: judgment about which papers are actually good. citation count and methodological rigor don’t correlate as much as we’d like. Claude identifies the landscape; you identify what matters in it.

the danger: taking Claude’s summary as a substitute for reading the paper. Claude compresses. compression loses nuance. the nuance is usually the interesting part. use the summaries to prioritize what to read, not to skip reading.

data analysis

this is where Claude Code earns its keep in research. not because it’s a better statistician than you (it’s not), but because it eliminates the gap between “I know what analysis to run” and “I remember the exact pandas/R syntax to run it.”

real workflow:

"I have a CSV with 50,000 rows of survey responses. 
columns: age, gender, education_level, response_1 through response_20, 
completion_time. I need descriptive stats, check for missing data patterns, 
and run a factor analysis on the response columns. 
use Python with pandas and scipy."

Claude writes the script. runs it. shows you the output. you look at the factor loadings and say “that doesn’t look right, try varimax rotation.” Claude adjusts. iterate until the analysis matches your methodological requirements.

the speed improvement is real: going from analysis plan to preliminary results in an afternoon instead of two days of syntax debugging. but — and this is critical — you need to understand the statistics. Claude will happily run an inappropriate analysis on your data and present the results with confidence. it doesn’t know your research questions. it doesn’t know your field’s conventions about what tests are appropriate. it just runs what you ask.

rule: if you can’t explain why the analysis is correct, don’t use the results. Claude is a fast calculator, not a methodologist.

paper writing assistance

controversial territory. here’s my honest take.

Claude Code is genuinely useful for:

→ methods sections. describe your analysis pipeline and Claude writes a clear, reproducible methods section. these sections are formulaic by design — that’s what makes them reproducible. Claude handles formulaic well.

→ formatting. converting between LaTeX templates, fixing bibliography formats, generating tables from data. the mind-numbing parts of academic writing that have nothing to do with thinking.

→ first drafts of structured sections. related work summaries, results descriptions, supplementary materials. sections with clear structure and low creative demand.

Claude Code is risky for:

→ introduction and discussion. these sections require understanding your contribution’s significance, the field’s context, and the nuanced arguments about why your work matters. Claude produces plausible-sounding versions that often miss the actual point.

→ anything involving claims about your results. Claude will happily write “our results demonstrate a significant relationship between X and Y” when your p-value is 0.06. it doesn’t understand the politics of “demonstrate” vs “suggest” vs “indicate” in your field.

→ novelty claims. Claude doesn’t know what’s actually novel. it will describe your incremental improvement as groundbreaking if you let it.

reproducibility tooling

here’s an underrated use case. Claude Code can help build reproducibility infrastructure:

→ write setup scripts that recreate your analysis environment → generate requirements files from your import statements → create data processing pipelines with clear input/output documentation → build README files for your data repositories that explain what each file contains

making research reproducible is tedious work that researchers chronically skip. Claude makes it fast enough that you might actually do it.

the ethics footnote

using AI in research requires disclosure. most journals now have AI use policies. most are vague. the safe approach: disclose what you used, how you used it, and where in the process. “Claude Code was used for data processing scripts and formatting; all analysis decisions and interpretations are the authors’” is a reasonable template.

the uncomfortable question: where does “used for formatting” end and “used for writing” begin? I don’t have a clean answer. neither does your field. but transparency is better than the alternative, and pretending researchers aren’t using these tools helps nobody.

what part of your research workflow burns the most time for the least intellectual contribution?

→ data portability — owning your research data → context engineering — feeding Claude the right context → claude code setup — getting started

Ray Svitla stay evolving