Autonomous academic research pipeline: deep research, computational experiments, paper writing, peer review, and iterative revision
You are the Abstract Bilingual Agent. You write high-quality bilingual abstracts (English + Traditional Chinese) with keywords for academic papers. Each language version is independently composed — never a mechanical translation of the other. You are activated in Phase 5b (parallel with citation_compliance_agent).
You are the Argument Builder Agent. You construct the paper's argumentative backbone: central thesis, sub-arguments, claim-evidence-reasoning (CER) chains, counter-arguments, and logical flow. You are activated in Phase 3 and produce the Argument Blueprint that guides the draft_writer_agent.
You are the Bibliography Agent. You conduct systematic, reproducible literature searches. You identify relevant sources, apply inclusion/exclusion criteria, create annotated bibliographies in APA 7.0 format, and document the search strategy for reproducibility.
You are the Citation Compliance Agent. You verify all citations in the paper draft for format correctness, cross-reference in-text citations against the reference list, check DOIs/URLs, and auto-correct detected errors. You are activated in Phase 5a (parallel with abstract_bilingual_agent).
Use this agent when a major project step has been completed and needs to be reviewed against the original plan and coding standards. Examples: <example>Context: The user is creating a code-review agent that should be called after a logical chunk of code is written. user: "I've finished implementing the user authentication system as outlined in step 3 of our plan" assistant: "Great work! Now let me use the code-reviewer agent to review the implementation against our plan and coding standards" <commentary>Since a major project step has been completed, use the code-reviewer agent to validate the work against the plan and identify any issues.</commentary></example> <example>Context: User has completed a significant feature implementation. user: "The API endpoints for the task management system are now complete - that covers step 2 from our architecture document" assistant: "Excellent! Let me have the code-reviewer agent examine this implementation to ensure it aligns with our plan and follows best practices" <commentary>A numbered step from the planning document has been completed, so the code-reviewer agent should review the work.</commentary></example>
Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, and Socratic guided modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review.
Academic paper writing skill with 12-agent pipeline. v2.5: Style Calibration (learn author's writing voice from past papers) + Writing Quality Check (writing quality checklist for natural prose). Supports IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures. APA 7.0 (default), Chicago, MLA, IEEE, Vancouver citation formats. Bilingual abstracts (zh-TW + EN). Multi-format output (LaTeX, DOCX, PDF, Markdown). Triggers on: write paper, academic paper, paper outline, write abstract, revise paper, check citations, convert to LaTeX, guide my paper, parse reviews, revision roadmap, 寫論文, 學術論文, 論文大綱, 寫摘要, 修改論文, 檢查引用, 引導我寫論文, 帶我規劃論文, 逐章規劃, 論文架構, 審查意見, 修訂路線圖.
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 9-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
Fully autonomous academic research pipeline. Takes a research topic and target venue, then runs without stopping: deep research -> computational experiments (autoresearch-style keep/discard loop) -> paper writing -> integrity verification -> peer review -> iterative revision with tiered escalation -> final integrity check -> finalize. 8-stage unified pipeline: INIT, RESEARCH, EXPERIMENT, WRITE, INTEGRITY, REVIEW, FINAL_INTEGRITY, FINALIZE. Dual-loop architecture with mandatory integrity gates. Triggers on: auto-academic, autonomous research, auto research pipeline, full autonomous paper, research to accepted paper.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Uses power tools
Uses Bash, Write, or Edit tools
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A Claude Code plugin that runs a fully autonomous academic research pipeline. Give it a topic and venue — it handles research, experiments, paper writing, peer review, and revision without stopping until the paper is accepted or safety caps are hit.
/plugin marketplace add waterwoods-ai/auto-academic
/plugin install auto-academic@waterwoods-ai
/reload-plugins
git clone https://github.com/waterwoods-ai/auto-academic.git
cp -r auto-academic/ ~/.claude/plugins/auto-academic/
No external plugin dependencies. Everything is bundled.
/auto-academic "Impact of LLMs on undergraduate writing skills" --venue "Computers & Education"
That's it. The pipeline runs autonomously from research to finalized paper. You can walk away — it will not stop to ask you questions.
Auto-academic runs a fully autonomous 8-stage pipeline with two loops:
INIT → RESEARCH → EXPERIMENT → WRITE → INTEGRITY → REVIEW → FINAL_INTEGRITY → FINALIZE
↑ |
└── inner loop (autonomous) ────────────────┘
|
REVIEW ←→ REVISE (outer loop)
Inner loop (Stages 1-2): Deep research produces hypotheses. Computational experiments validate them using an autoresearch-style keep/discard mechanism. A convergence judge exits the loop when all hypotheses have evidence.
Outer loop (Stage 5): Simulated peer review produces revision requests. The paper is revised and re-reviewed. If a Major/Reject decision persists at round 3, an escalation analyst sends specific concerns back to the inner loop for targeted re-experimentation.
Integrity gates (Stages 4 and 6): Before and after review, a 5-phase verification checks every reference, citation, statistic, and claim in the paper.
/auto-academic "your research topic" --venue "Target Journal"
Run auto-academic on the effects of AI tutoring in STEM education, targeting Internet and Higher Education
| Argument | Required | Default | Description |
|---|---|---|---|
| topic | Yes | — | The research question or area |
| venue | No | APA 7.0, Tier 3 (SSCI) | Target publication venue |
/auto-academic "Does retrieval practice improve long-term retention in online courses?" --venue "Computers & Education"
/auto-academic "Transformer architectures for low-resource NLP" --venue "ACL"
/auto-academic "Agent-based modeling of misinformation spread" --venue "Nature Human Behaviour"
/auto-academic "Effectiveness of gamification in K-12 math education"
The last example uses the default venue (Tier 3 SSCI journal with APA 7.0 formatting).
The pipeline adapts rigor, format, and experiment expectations to your target venue:
| Tier | Examples | Citation | Experiment Expectations |
|---|---|---|---|
| 1 | NeurIPS, ICML, ACL, CVPR | Conference style | Ablations, multiple seeds, SOTA comparison, reproducible code |
| 2 | Nature, Science | Numbered | Pre-registration, effect sizes + CI, replication checks |
| 3 | Computers & Education, Internet & Higher Education | APA 7.0 | Survey validation, effect sizes, IRB mention, mixed methods OK |
| 4 | Regional journals (JRES, Taiwan TESOL) | APA 7.0 | Appropriate methodology, standard rigor |
| 5 | Workshop/conference proceedings | Varies | Preliminary results acceptable |
If your venue isn't in the database, the pipeline researches it via web search and assigns the closest tier.
Parses your topic and venue, looks up the venue profile, creates a timestamped workspace directory, initializes git tracking, and sets up pipeline state.
If an interrupted run for the same topic is detected, offers to resume from where it stopped.
npx claudepluginhub waterwoods-ai/auto-academicAccess Zotero reference library via local API — list, search, read PDFs, export BibTeX
Production-grade academic research pipeline for Claude Code: research → write → review → revise → finalize. 4 skills, 27 modes, 39-agent ensemble, v3.7.3 + v3.8 L3 claim-faithfulness gate, v3.9.0 cross-index triangulation, v3.10 triangulation policy layer, v3.11 deterministic citation verification gate (#182).
Semi-automated research assistant for academic research and software development, with skills for literature review, experiments, analysis, writing, and project knowledge management
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Academic paper writing skills for ML conferences (NeurIPS, ICML, ICLR, AAAI)
PhD-level research capabilities: literature review, multi-source investigation, critical analysis, hypothesis-driven exploration, quantitative/qualitative methods, and lateral thinking