academic-research-skills for Claude Code

I ran across the GitHub project “academic-research-skills”, which packages a practical Claude Code workflow into a constrained loop: research, write, review, revise, finalize.

This is the kind of thing I care about because it reduces the “agent drift” problem—LLMs can start producing plausible prose without verifying whether the underlying claims are actually supported. The loop enforces a cadence where output quality is improved iteratively, not in one shot.

A concrete situation: when I’m validating a technical blog post or internal doc that depends on multiple sources (papers, benchmark pages, specs), I’ll often use Claude Code to gather citations and draft sections. The workflow gives me an explicit checkpointing pattern so I can ask for review passes before I commit to final wording.

What to look at first: - the top-level entry (README) that describes the end-to-end loop - the “research → write” handoff (how sources are gathered and then transformed into draft text) - the “review → revise” stage (what the reviewer step should verify) - any included prompts/templates that define the required outputs per phase

If you’re building or operating agent systems, this is also a useful reference for how to structure multi-step tasks with clear phase boundaries. It’s not a new model or a benchmark claim—just a workflow that’s easy to lift into your own pipelines.