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How to Automate Academic Research with Claude Code and Python Scripts

Build a research digest that searches arXiv daily and Google Scholar weekly, filters results against your own topics, and turns selected PDFs into rigorous summaries of methods, findings, effect sizes, and limitations.

How to Automate Academic Research with Claude Code and Python Scripts

Source episode · 15:54

How I AI: Teresa Torres's Claude Code System for Task Management, Automated Research, and 'Lazy' Prompting

with Claire Vo

Teresa shows a daily arXiv digest, her manual PDF filter, and two scheduled Python scripts that find new papers and ask Claude Code agents to summarize the ones she saves.

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Before you start

What you need

  • A list of research topics and keywords
  • Python and a scheduler such as cron
  • A research folder with sources and notes subfolders
  • Claude Code access for paper summaries

What you’ll make

A daily Markdown digest of new papers plus detailed summaries for the PDFs you choose to save.

Tools used

Step by step

The workflow

Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.

4 steps

Step01

Set Up an Automated Search Script

Create a topic configuration with the subjects and keywords you follow. Write one Python script that searches arXiv each morning and Google Scholar on a slower cadence, such as weekly. Record each paper identifier so later runs can separate new results from papers you have already seen.

Example prompt
Build a Python research searcher driven by a YAML topic file. Search arXiv every day and Google Scholar once a week, deduplicate results by stable paper ID or URL, and write only new results to a dated Markdown digest with title, authors, abstract, source, and link. Include setup and cron examples.
Step02

Manually Curate and Save Papers

Spend a few minutes scanning the digest. Download only the papers worth deeper analysis, then save each PDF inside the matching topic folder under sources. Keep notes in a neighboring notes folder so the original paper and Claude generated analysis stay distinct.

Step03

Create an Automated Summarization Script

Write a second scheduled script that scans the source folders for new PDFs each night. For every unprocessed file, start a Claude Code summarization task, save the result in the matching notes folder, and add it to the next research digest.

Example prompt
Build a nightly Python script that finds unprocessed PDFs under research/*/sources, invokes a Claude Code agent with my paper summary skill, saves each Markdown summary under the matching notes folder, and writes a manifest so successful files are not processed twice. Report failures without marking them complete.
Step04

Engineer a High-Quality Summarization Prompt

Create a paper summary skill built for critical reading. Require the study design, sample, measures, main result, effect size when reported, limitations, and relevance to your topic. Preserve uncertainty and make missing evidence explicit instead of filling gaps.

Example prompt
Analyze this academic paper for critical review. Return: 1. Core claim, 2. Study design and sample, 3. Measures and comparison groups, 4. Main findings with the reported numbers and effect sizes, 5. Limitations and threats to validity, 6. Relevance to [topic]. If the paper does not report an item, say so. Distinguish the authors' claims from your assessment.

What good looks like

  • Previously seen papers do not reappear as new results.
  • Each saved PDF receives a summary by the next digest run.
  • Summaries include the method, sample, findings, effect size when reported, and limitations.
  • The daily review remains short enough to act as a useful filter.

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After the steps

Runbook notes

How to recover when the loop fails and where human judgment helps.

Recover

If it goes sideways

The search digest contains too many irrelevant papers
Tighten the topic configuration with phrases, exclusions, and narrower keywords. Keep the manual PDF selection step so only promising papers enter the summarization queue.
The same papers keep returning
Store a stable paper identifier or normalized URL after every search and check it before writing the next digest.
A saved PDF is not summarized
Confirm that the file is in the expected sources folder, that the nightly script can read it, and that the file was not already marked as processed. Rerun the script for that file.
The summary merely restates the abstract
Strengthen the skill to require study design, sample size, measures, quantitative results, effect size when available, and limitations. Tell Claude to say when the paper does not report an item.

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