Claude for Life Sciences - Build 7 Research Workflows

You asked Claude for the literature. It handed you three papers that do not exist.



You ran the pipeline it wrote. It worked yesterday. Today it breaks. Again.



Meanwhile, a university professor just tore up his own rule. For years he taught students to never let AI near their citations. Then he ran Claude Science across three real projects, work that would normally have taken him weeks. A neuroscientist used it to find new discoveries inside a dataset she had already published. Another scientist rebuilt a $7,500-a-year structural-biology suite for free and gave it away. The difference is not that they are better scientists than you. It is that they built their workflows once, then made the AI prove every result.



This book hands you seven research workflows you build once and run for the rest of your career.



Your connectors are wired the first afternoon, and your first verified literature review runs in under an hour, every citation resolved to a real paper or thrown out. By the end of Week 1, your data cleaning and your first analysis script run on your own files, with an integrity check that catches the silent corruption. By Week 2, that script is a pipeline that returns the same answer on any machine, and a reviewer agent audits your work for the errors that slip past peer review. By Day 30, you have a personal research system and a methods section drafted from results you can defend.



This is not another AI-for-researchers overview.


Every workflow ships with a free companion GitHub repo: the four Skills, the connector table, and the checklists at the exact paths the book uses, plus two sample runs so you can watch the verification fail on a real error before you ever point it at your own work.


Here is what you will build:

1. A connected Claude research setup and your first reusable Skill, running the first afternoon.

2. A literature review workflow that resolves every citation to a real paper or kills it.

3. A data-cleaning workflow with an integrity check, so faster never quietly becomes wrong.

4. A runnable analysis script for the task you repeat every week.

5. A reproducible pipeline that returns the same result on any machine, on any day.

6. A reviewer agent that audits your analysis for data leakage, dropped controls, and overstated significance.

7. A publication workflow: the figure, the methods draft, and the AI-disclosure statement journals now demand.



Plus the cost model that prices what your setup replaces, the way one scientist replaced a $7,500-a-year suite, and the paperwork that opens the $50,000 credit programs for rare-disease and biotech work.



This is 150-plus pages of do-this-now workflows, real connectors, and the verification layer this field will punish you for skipping. Scroll up and grab your copy.