Academic Research Skills: Scientific Workflow Suite for Claude Code

RepositoryImbad0202/academic-research-skillsTool

Academic Research Skills (ARS) is a modular skill suite for Claude Code designed to guide researchers through the entire paper pipeline, from initial research and drafting to review, revision, and finalisation. The toolkit implements a human-in-the-loop philosophy to guard against typical AI failure modes like citation hallucinations.

Key points

  1. Plugin suite for Claude Code (CLI, VS Code, JetBrains from v3.7.0) featuring interactive prompts like /ars-plan for Socratic paper planning.
  2. Human-in-the-loop design: Automates reference discovery, citation formatting, and consistency checks without attempting full autonomous ghostwriting.
  3. Quality and integrity gates: Stage 2.5 and 4.5 checks evaluate manuscripts against a seven-mode failure checklist targeting fabricated methodologies and citation errors.
  4. Style Calibration and Writing Quality Checks help preserve the author's personal tone and eliminate synthetic, machine-sounding prose patterns.

Why it matters

Fully autonomous scientific research pipelines regularly suffer from hallucinations, shortcut reasoning, and phantom references. ARS provides structured guardrails, treating Claude Code strictly as a copilot that accelerates tedious chores while keeping analytical responsibility with the researcher.

Analysis

At version 3.21.1 with over 45,000 GitHub stars and a Zenodo DOI, the repository shows high maturity and broad adoption in the academic AI community. It relies on Python and Claude Code's plugin interface under a CC BY-NC 4.0 license. The tool is geared towards scientists, PhD candidates, and academic writers seeking to leverage AI for productivity without compromising peer-review standards.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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