From Topic to Submission: Claude Code’s ARS Pipeline for Academic Papers

Academic Research Skills (ARS) is an open‑source Claude Code skill collection that covers the full research‑to‑submission pipeline, from topic selection to final formatting. The author shares the GitHub repository github.com/Imbad0202/academic-research-skills and describes the tool’s philosophy: AI acts as a copilot, handling repetitive tasks such as reference gathering, citation formatting, data verification, and logical consistency checks, while leaving the core scientific thinking to the human researcher.

AI is your copilot, not the pilot – the tool does not write the paper for you; it assists with the “dirty work” so you can focus on problem definition, method selection, data interpretation, and articulating your conclusions.

Why Human‑in‑the‑Loop?

The author cites Lu et al. (2026) “The AI Scientist” (Nature 651: 914‑919), which achieved a 6.33/10 score in an ICLR 2025 workshop but listed many limitations: bugs, hallucinations, shortcuts, mistaking bugs for insights, fabricated methodology, framework lock‑in, and especially citation hallucinations. These failures motivate ARS’s design principle that a human researcher plus AI augmentation avoids such pitfalls.

Core Capabilities

Deep Research (13 agents): Socratic guidance, PRISMA systematic reviews, intent recognition, dialogue health monitoring, Semantic Scholar verification.

Academic Paper (12 agents): Style calibration, writing quality check, LaTeX hardening, visualization, revision assistance, citation conversion.

Academic Paper Reviewer (7 agents): Editor‑in‑Chief simulation, dynamic reviews, Devil’s Advocate, 0‑100 scoring, attack‑strength retention, R&R traceability matrix.

Academic Pipeline (10 stages): Full‑process orchestration with adaptive checkpoints, material passport, optional repro_lock, and cross‑model integrity verification.

Installation

Requirements: latest Claude Code, exported ANTHROPIC_API_KEY, optional Pandoc (DOCX export) and Tectonic + Source Han Serif TC (APA 7.0 PDF export).

/plugin marketplace add Imbad0202/academic-research-skills
/plugin install academic-research-skills

Codex CLI users can clone the equivalent repository:

# Codex version, same workflow packaged as a single skill
gh repo clone Imbad0202/academic-research-skills-codex

Usage

After installation, run commands such as:

/ars-plan                # plan topic and chapter structure
/ars-lit-review "your research topic"   # generate literature review

Full pipeline commands include: /ars-plan: topic and chapter planning /ars-lit-review: literature review

Data/method verification (Stage 2.5 gate)

Writing and style alignment (Style Calibration uses your past papers as corpus)

Review mode with calibration against a manually labeled gold standard

Final formatting (APA 7.0 PDF/DOCX)

Cost

A 15 k‑word paper running the full 10‑stage pipeline costs roughly $4–6 in token usage.

The author notes this is reasonable for a Claude Code Pro subscription, keeping the cost of a small master’s thesis fully controllable.

Surprising Data

The README’s showcase reports a post‑publication audit that uncovered 21 out of 68 citation issues , meaning even after three rounds of ARS’s built‑in citation checks, about one‑third of problems remain undetected.

This reinforces the author’s stance on human‑in‑the‑loop: AI will always miss some errors, but reducing the miss rate from 100 % to ~30 % and letting a human finalize the work yields a practical workflow.

Conclusion

ARS is ideal for two audiences:

Graduate students – benefit from topic selection, literature review, and style alignment.

Chinese researchers publishing in English – Style Calibration learns the author’s or advisor’s English style, reducing the “AI‑generated” feel.

The most valuable design element is the “quality gate”: each stage forces an integrity check, preventing the AI from completing the entire pipeline unchecked. This pattern is applicable to other long‑chain agent systems; see the ai_research_failure_modes.md checklist for transferable ideas.

The project is released under CC BY‑NC 4.0, allowing non‑commercial academic use.