The most headache-inducing problem for academic agents is not the inability to write, but the inability to ensure credibility once written. This project directly adds an auditable academic pipeline to Claude Code: from research to writing to peer review responses, each stage has hard checkpoints – for example, using the Four Treasuries citation verification to check authenticity, using experiment declaration alignment to prevent exaggeration, using review response audit to ensure item-by-item coverage. It doesn’t rely on prompting to coax the model; it encodes the verification logic as executable protocols.

Imbad0202/academic-research-skills

Source: https://github.com/Imbad0202/academic-research-skills

Academic Research Skills for Claude Code

Version (https://github.com/Imbad0202/academic-research-skills/releases/tag/v3.12.1) DOI (https://doi.org/10.5281/zenodo.20696614)
License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
Sponsor (https://buymeacoffee.com/crucify020v)

简体中文版 | 繁體中文版 | 日本語版

A comprehensive suite of Claude Code skills for academic research, covering the full pipeline from research to publication.

Install in 30 seconds (Claude Code CLI / VS Code / JetBrains, v3.7.0+):
text /plugin marketplace add Imbad0202/academic-research-skills /plugin install academic-research-skills

Then try /ars-plan to walk through your paper structure via Socratic dialogue, or jump to Quick install for prerequisites and the traditional symlink flow.

AI is your copilot, not the pilot. This tool won’t write your paper for you. It handles the grunt work — hunting down references, formatting citations, verifying data, checking logical consistency — so you can focus on the parts that actually require your brain: defining the question, choosing the method, interpreting what the data means, and writing the sentence after “I argue that.”

Unlike a humanizer, this tool doesn’t help you hide the fact that you used AI. It helps you write better. Style Calibration learns your voice from past work. Writing Quality Check catches the patterns that make prose feel machine-generated. The goal is quality, not cheating.

Why human-in-the-loop, not full automation?

Lu et al. (2026, Nature 651:914-919) built The AI Scientist — the first fully autonomous AI research system to publish a paper through blind peer review at a top-tier ML venue (ICLR 2025 workshop, score 6.33/10 vs workshop average 4.87). Their Limitations section enumerates the failure modes that any fully-autonomous AI research pipeline inherits: implementation bugs, hallucinated results, shortcut reliance, bug-as-insight reframing, methodology fabrication, frame-lock, citation hallucinations. ARS is built on the premise that a human researcher augmented by AI avoids these failure modes better than either alone. Stage 2.5 and Stage 4.5 integrity gates run a 7-mode blocking checklist (see academic-pipeline/references/ai_research_failure_modes.md); the reviewer offers an opt-in calibration mode that measures its own FNR/FPR against a user-supplied gold set.

Zhao et al. (https://arxiv.org/abs/2605.07723) (2026-05) audited 111M references across 2.5M papers on arXiv, bioRxiv, SSRN, and PMC. Their conservative estimate is 146,932 hallucinated citations for 2025 alone, with an observed mid-2024 inflection; for the bioRxiv-to-PMC pairing they report 85.3% preprint-to-published persistence. The paper describes “real citations deployed to support claims the cited references do not actually make” as an open challenge. ARS v3.7.1 added trust-chain frontmatter for source provenance; v3.7.3 added locator infrastructure (three-layer citation anchors) for future claim-level audits and surfaces advisory risk signals at cite time (ARS labels the claim-faithfulness gap internally as “L3”; this is ARS terminology, not the paper’s). v3.7.x is motivated by Zhao et al.’s corpus-scale findings; corpus-scale evaluation of ARS itself remains future work. v3.8 closes the second half of the L3 gap. v3.7.3 made every citation carry a locator anchor; v3.8 adds an opt-in audit pass (ARS_CLAIM_AUDIT=1) that fetches the cited source against each anchor and judges whether the claim is actually supported. Five new HIGH-WARN classes (claim-not-supported, negative-constraint-violation, fabricated-reference, anchorless, constraint-violation-uncited) gate-refuse output through the formatter terminal hard gate. Calibration is shipped as a 20-tuple gold set with FNR<0.15 + FPR<0.10 acceptance thresholds; ramp-on plan is deferred to post-calibration evidence per v3.8 spec §5.

v3.3 was inspired by PaperOrchestra (https://arxiv.org/abs/2604.05018) (Song, Song, Pfister & Yoon, 2026, Google): Semantic Scholar API verification, anti-leakage protocol, VLM figure verification, and score trajectory tracking.

Architecture & pipeline

👉 docs/ARCHITECTURE.md — the full pipeline view: flow diagram, stage-by-stage matrix, data-access flow, skill dependency graph, quality gates, and mode list. The architecture doc supersedes the sprawling pipeline description that used to live here. Everything about what runs in which stage now lives in one place.

Quick install

Prerequisites

• Claude Code (https://docs.claude.com/en/docs/claude-code/setup) (latest; plugin packaging requires recent versions)
• ANTHROPIC_API_KEY exported, or set on first claude run
• Optional: Pandoc for DOCX, tectonic + Source Han Serif TC for APA 7.0 PDF (Markdown output works without either)

Plugin install (v3.7.0+, recommended):
text /plugin marketplace add Imbad0202/academic-research-skills /plugin install academic-research-skills

Verify it works: run /ars-plan and describe a paper you’re working on — ARS will start a Socratic dialogue to map out chapter structure. For a single-shot test instead, try /ars-lit-review "your topic".

👉 docs/SETUP.md — full guide: install Claude Code, set up API keys, optional Pandoc/tectonic for DOCX/PDF, cross-model verification (ARS_CROSS_MODEL), and five installation methods (Plugin, project skills, global skills, claude.ai Project, repo-cloned).

Using Codex CLI? Install the sibling distribution instead: Imbad0202/academic-research-skills-codex (https://github.com/Imbad0202/academic-research-skills-codex) — same workflow content, Codex-native packaging as a single $academic-research-suite skill with ars-* aliases.

Performance & cost

👉 docs/PERFORMANCE.md — per-mode token budgets, full-pipeline estimate (~$4–6 for a 15k-word paper), and recommended Claude Code settings (Skip Permissions; Agent Team optional).

Guides & articles

• Academic Writing Shouldn’t Be a Solo Act (https://open.substack.com/pub/edwardwu223235/p/academic-writing-shouldnt-be-a-solo?r=4dczl&utm_medium=ios) — full pipeline walkthrough (English)
• Academic Writing Shouldn’t Be a Solo Act: How an Open-Source AI Collaboration Tool Changes Researchers’ Workflow (https://open.substack.com/pub/edwardwu223235/p/ai?r=4dczl&utm_medium=ios) — Full Usage Guide (Traditional Chinese)

Features at a glance

• Deep Research — 13-agent research team with Socratic guided mode, PRISMA systematic review, intent detection, dialogue health monitoring, optional cross-model DA, Semantic Scholar API verification.
• Academic Paper — 12-agent paper writing with Style Calibration, Writing Quality Check, LaTeX hardening, visualization, revision coaching, citation conversion, anti-leakage protocol, and VLM figure verification.
• Academic Paper Reviewer — 7-agent multi-perspective peer review with 0–100 quality rubrics (EIC + 3 dynamic reviewers + Devil’s Advocate), concession threshold protocol, attack intensity preservation, optional cross-model DA critique / calibration, R&R traceability matrix, read-only constraint.
• Academic Pipeline — 10-stage pipeline orchestrator with adaptive checkpoints, claim verification, Material Passport, optional repro_lock, optional cross-model integrity verification, mid-conversation reinforcement, and score trajectory tracking.
• Data Access Level Metadata (v3.3.2+) — every skill declares data_access_level (raw / redacted / verified_only); enforced by scripts/check_data_access_level.py. Pattern adapted from Anthropic’s automated-w2s-researcher (2026). See shared/ground_truth_isolation_pattern.md.
• Task Type Annotation (v3.3.2+) — every skill declares task_type (open-ended or outcome-gradable). All current ARS skills are open-ended.
• Benchmark Report Schema (v3.3.5+) — JSON Schema + lint for honest benchmark comparisons. See shared/benchmark_report_pattern.md.
• Artifact Reproducibility Lockfile (v3.3.5+) — optional repro_lock sub-block on Material Passport. Configuration documentation, not replay guarantee — LLM outputs are not byte-reproducible. See shared/artifact_reproducibility_pattern.md.
• Experiment Provenance Intake (#260) — optional experiment_provenance[] on the Material Passport records experiments the scholar ran externally (ARS never runs experiments), and manuscript claims join to them via claim_intent_manifest.planned_experiment_ids[]. The integrity gate (Stage 2.5/4.5) audits each experiment-backed claim against declared provenance — ALIGNED / OVERSTATED / NOT_SUPPORTED_BY_PROVENANCE / PROVENANCE_INSUFFICIENT — without judging whether the experiment itself was correct. A fail-closed experiment_intake_declaration makes “did you run experiments?” an explicit Stage 1 decision (even literature-only runs declare no_experiments_declared). See shared/handoff_schemas.md §“Experiment Provenance Intake (#260)”.

Showcase: real pipeline output

See the complete artifacts from a real 10-stage pipeline run — peer review reports, integrity verification reports, and the final paper:
Browse all pipeline artifacts →

Companion: Experiment Agent

If your research involves running experiments (code or human studies) before writing, the Experiment Agent (https://github.com/Imbad0202/experiment-agent) skill fills the gap between ARS Stage 1 (RESEARCH) and Stage 2 (WRITE).

ARS Stage 1 RESEARCH → RQ Brief + Methodology Blueprint ↓ experiment-agent → run/manage experiments → validate results ↓ ARS Stage 2 WRITE → write paper with verified experiment results

What it does: executes code experiments (Python, R, etc.) with real-time monitoring, manages human study protocols with IRB ethics checklist, interprets statistics with 11-type fallacy detection, and verifies reproducibility.

How to use together: pause the ARS pipeline after Stage 1, run experiments in a separate experiment-agent session, then bring the results (with Material Passport) back to ARS Stage 2. ARS requires zero modification. See the experiment-agent README (https://github.com/Imbad0202/experiment-agent) for setup instructions.

Stage 1 intake declaration (#260): at Stage 1, ARS detects whether the run will carry experiment-backed claims and sets a fail-closed experiment_intake_declaration on the Material Passport. If you ran experiments externally, the scholar enters one experiment_provenance[] entry per experiment (experiment_id, nested repro_lock, planned_vs_executed[], negative_results[], known_limitations[]) and the declaration is set to experiments_declared; if not, it is set to no_experiments_declared. The declaration is required on every post-#260 passport — a run that touches no experiments still declares no_experiments_declared, so the integrity gate can never be silently bypassed by a forgotten provenance block. The experiment_ids are frozen at this intake point; the writers later reference them via planned_experiment_ids[].

Teaching-side companion: Teaching Skills (https://github.com/YujxZJCN/teaching-skills) applies the ARS architecture (skill ensembles, shared contracts, staged gates, a Course Passport) to the teaching side of academic life — course design → lessons → assessment → delivery → reflection; its sotl mode hands classroom-inquiry projects off to ARS deep-research / academic-paper for the publication phase.

Usage

Quick Start

``

Start a full research pipeline

You: “I want to write a research paper on AI’s impact on higher education QA”

Start with Socratic guidance

You: “Guide my research on AI in educational evaluation”

Write a paper with guided planning

You: “Guide me through writing a paper on demographic decline”

Review an existing paper

You: “Review this paper” (then provide the paper)

Check pipeline status

You: “status”
``

Individual Skills

Deep Research (8 modes)

"Research the impact of AI on higher education" → full mode "Give me a quick brief on X" → quick mode "Do a systematic review on X with PRISMA" → systematic-review mode "Guide my research on X" → socratic mode (guided) "Fact-check these claims" → fact-check mode "Do a literature review on X" → lit-review mode "Compare these papers in WHY/HOW/WHAT format" → three-way-scan mode "Review this paper's research quality" → review mode

Academic Paper (11 modes)

"Write a paper on X" → full mode "Guide me through writing a paper" → plan mode (guided) "Build a paper outline" → outline-only mode "I have a draft, here are reviewer comments" → revision mode "Parse these reviewer comments into a roadmap" → revision-coach mode "Write an abstract for this paper" → abstract-only mode "Turn this into a literature review paper" → lit-review mode "Convert to LaTeX" / "Convert citations to IEEE" → format-convert mode "Check citations" → citation-check mode "Generate an AI disclosure statement for NeurIPS" → disclosure mode "Audit my rebuttal draft against the reviews" → rebuttal-audit mode

Academic Paper Reviewer (6 modes)

"Review this paper" → full mode (EIC + R1/R2/R3 + Devil's Advocate) "Quick assessment of this paper" → quick mode "Guide me to improve this paper" → guided mode "Check the methodology" → methodology-focus mode "Verify the revisions" → re-review mode "Calibrate this reviewer against my gold set" → calibration mode

Academic Pipeline (Orchestrator)

"I want to write a complete research paper" → full pipeline from Stage 1 "I already have a paper, review it" → mid-entry at Stage 2.5 (integrity first) "I received reviewer comments" → mid-entry at Stage 4

Pipeline ends with Stage 6: Process Summary — auto-generates a paper creation process record with 6-dimension Collaboration Quality Evaluation (1–100 scoring).

Supported Languages

• Traditional Chinese (繁體中文) — default when user writes in Chinese
• English — default when user writes in English
• Bilingual abstracts (Chinese + English) for academic papers

Using a different language? Socratic mode (deep-research) and Plan mode (academic-paper) use intent-based activation — they detect the meaning of your request, not specific keywords. This means they work in any language without modification.

However, the general Trigger Keywords section (which determines wheth