Open Academic Paper Machine

A Claude Code plugin that autonomously writes academic papers — from literature search to production-ready LaTeX/PDF.

v6.4.0 — Paper-vs-Code Audit. New /audit-paper command and audit-engine skill: static audit of a paper's empirical claims (datasets, models, hyperparameters, metrics) against the linked code repository. Classifies each claim as CONFIRMED / PARTIAL / MISSING / MISMATCH / NOT_AUDITABLE with file:line evidence. Complements /verify-citations (external sources) by checking own-work claims against own code. Inspired by Feynman's /audit.

Scope note. This project is a technical contribution: it explores what is possible with current LLM technology for academic paper production, not what is desirable or ethically permissible. The ethical, epistemological, and policy questions raised by AI-generated academic writing — authorship attribution, academic integrity, epistemic status, potential misuse — are important but outside the scope of this tool. They are addressed in the companion position paper (Blask & Funk, 2026).

Quick Start

# 1. Install the plugin
/plugin marketplace add TobiasBlask/open-paper-machine
/plugin install open-academic-paper-machine@open-paper-machine

# 2. Install dependencies
pip install paperbanana[google] academic-search-mcp

# 3. Set up your API key (free — https://aistudio.google.com/apikey)
echo 'GOOGLE_API_KEY="your-key"' > ~/.paperbanana.env
# Or per-project: echo 'GOOGLE_API_KEY="your-key"' > .env

# 4. Go
/write-paper The impact of generative AI on organizational decision-making

That's it. The plugin ships the academic-search MCP server, the PaperBanana direct Python API for figure generation, all 16 skill engines, 4 agents, 24 curated scientific skills, 20 slash commands, and the autonomous pipeline agent. Everything starts automatically.

Technical paper: The Open Academic Paper Machine: An Autonomous LLM Plugin for End-to-End Academic Paper Production (Blask, 2026) — describes the system architecture, design principles, and evaluation. LaTeX source in paper/.

Position paper: From Creator to Orchestrator? How an LLM Agent Wrote This Paper and What That Means for Science (Blask & Funk, 2026) — a position paper on AI-augmented knowledge production, orchestrated through human-AI interaction using this system. GitHub repo.

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How It Works

The machine runs autonomously through 9 phases:

Phase	What Happens	Your Job
0. Idea Evaluation (v6.3.0)	Stress-test idea along 7 dimensions, conclusion-first test, PURSUE/REFINE/KILL verdict	Decide whether to proceed
1. Reconnaissance	4-6 search queries across 4 academic APIs, snowballing, deduplication	Check scope, redirect if needed
2. Framing	Theory selection, gap formulation, research questions, contribution statement	Confirm direction
3. Structure	Concept matrix, paper structure, word budget	Approve structure
4. Production	Write every section as complete paragraphs + generate figures	Read along, adjust
5. Assembly	Compile all sections, quality self-assessment, status report	Start review
6. LaTeX & PDF	Convert to arxiv-style LaTeX, resolve citations, compile PDF	Download and submit
7. Verification (opt.)	Fetch source abstracts/PDFs, verify each citation claim	Review flagged mismatches
8. Revision (repeatable)	Extract reviewer/co-author feedback, classify, implement changes, recompile + latexdiff	Approve change plan

Core principle: Before producing, evaluate. The machine gates the pipeline — not every topic deserves months of work. Then it makes decisions and presents results. You steer at checkpoints.

Phase 8 closes the loop: send an annotated PDF from your co-author or paste reviewer comments, and the review-engine extracts, classifies, and implements all changes — then recompiles and generates a visual diff. The cycle repeats (Round 1 → 2 → 3 → ...) until acceptance.

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Requirements

Requirement	Version	Notes
Claude Code	Latest	CLI or IDE extension (VS Code / JetBrains)
Python	3.10+	For PaperBanana and academic-search MCP servers
LaTeX	Any recent	Optional — only needed for PDF compilation (/export-latex)
Google API key	Free	For AI figure generation — get one here

No Google API key? The plugin works fully without one — you just won't get AI-generated figures. The figure-engine falls back to matplotlib/seaborn. All other features (literature search, writing, LaTeX, revision, etc.) work independently.

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Installation

What Gets Installed

The plugin bundles one MCP server that starts automatically, plus PaperBanana via direct Python API:

Component	pip package	What it does
academic-search (MCP)	academic-search-mcp	Searches Semantic Scholar, OpenAlex, CrossRef, arXiv. Snowballing, BibTeX/CSV export.
PaperBanana (direct API)	paperbanana	AI figure generation via Google Gemini. Multi-agent pipeline with iterative refinement. Based on Zhu et al. (2026).

The academic-search MCP server is configured in plugin.json and starts when Claude Code loads the plugin. PaperBanana is called via direct Python API (scripts/paperbanana_direct.py) — the MCP transport layer was removed in v6.3.0 due to persistent reliability issues (timeouts, silent failures).

Academic foundation: The figure generation pipeline implements the methodology from PaperBanana: Automating Academic Illustration for AI Scientists (Zhu et al., 2026). The MCP integration uses the community implementation at llmsresearch/paperbanana. See also the official research repo.

Step-by-Step

1. Add the marketplace and install the plugin:

/plugin marketplace add TobiasBlask/open-paper-machine
/plugin install open-academic-paper-machine@open-paper-machine

2. Install Python dependencies:

pip install paperbanana[google] academic-search-mcp

3. Configure your Google API key (needed for AI figure generation):

Get a free key at Google AI Studio, then set it up (choose one):

# Option A — Global (recommended, works across all projects):
echo 'GOOGLE_API_KEY="your-api-key-here"' > ~/.paperbanana.env

# Option B — Per-project:
echo 'GOOGLE_API_KEY="your-api-key-here"' > .env

Key loading priority: Environment variable → ~/.paperbanana.env → project .env. Never commit .env to version control. See .env.example for a template.

4. Install LaTeX (for PDF compilation):

# macOS
brew install --cask mactex-no-gui

# Ubuntu/Debian
sudo apt-get install texlive-full

5. Start writing:

/write-paper Your Paper Title Here

Cowork Setup

1. Download the latest release ZIP from GitHub Releases
2. In Cowork, open the Plugins panel and click "+" → upload the ZIP
3. Set GOOGLE_API_KEY in plugin settings (optional — only needed for AI figures)
4. /write-paper Your Paper Title

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Idea Evaluation — Phase 0 (v6.3.0)

Most AI writing tools help you write papers. Phase 0 helps you decide which papers to write.

The previous pipeline took any topic and produced. Version 6.3.0 adds the missing gate: Is this paper the right use of my time? Based on Nicholas Carlini's research philosophy (How to Win a Best Paper Award) and the research-companion plugin by Andre Huang.

# Standalone idea evaluation
/evaluate-idea Can LLMs replace systematic literature reviews?

# Or just run /write-paper — Phase 0 runs automatically first
/write-paper The impact of generative AI on organizational decision-making

How It Works

Three specialist agents evaluate your idea before the pipeline commits to production:

Agent	Role	Output
Idea Critic	Adversarial stress-test along 7 dimensions	PURSUE / REFINE / KILL verdict
Research Strategist	Strategic viability: competition, timing, comparative advantage	Green / Yellow / Red flags
Brainstormer	Cross-field connections, assumption challenges, reframings	Alternative angles and extensions

The 7 Evaluation Dimensions

Dimension	Key Question	Signal
Novelty (RS1)	If you don't do this, how long until someone else does?	Weeks / Months / Years
Impact (RS2)	Can you write a compelling conclusion right now?	Low / Medium / High
Timing (RS8)	Is the field ready? Too early? Already crowded?	Too Early / Well-Timed / Too Late
Feasibility (RS4)	What's the riskiest assumption? Can you test it in a week?	High / Medium / Low Risk
Competition (RS7)	Who else is working on this? What's your advantage?	Crowded / Moderate / Open
Nugget (RS3)	Can you state the key insight in one sentence?	Clear / Fuzzy / Missing
Narrative	Can you tell a story that makes a skeptical reader care?	Compelling / Workable / Weak

The Conclusion-First Test

The decisive gate. Before investing, the engine writes the best-case conclusion: if everything works perfectly, what can this paper say? If the answer is hollow or generic — if it only says "our method achieves X% improvement" — the idea doesn't have enough impact. Kill it and move on.

Research Strategy Principles (RS1-RS8)

Eight principles guide evaluation (see principles/research-strategy.md):

• RS1 (Novelty Test): Favor problems where your unique skills create a months-to-years gap.
• RS2 (Conclusion-First Test): Write the conclusion before doing the research.
• RS3 (Nugget Test): One sentence. One idea. Every figure connects to it.
• RS4 (Fail Fast): Start with what's most likely to kill the project.
• RS5 (Kill Early): A working project with low impact is worse than a killed project.
• RS6 (Unreasonable Effort): Go to unreasonable lengths — but only AFTER RS4 and RS5.
• RS7 (Comparative Advantage): Research space is high-dimensional; find your unique corner.
• RS8 (Timing Awareness): Impact = skill x domain importance at this moment.

Evaluations persist to research-evaluations/*.md for cross-session continuity. Previously killed ideas are checked for changed conditions rather than re-evaluated from scratch.

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Commands

Idea Evaluation (v6.3.0)

Command	Description
/evaluate-idea [topic]	Full idea stress-test — 7 dimensions, 3 agents, conclusion-first test, PURSUE/PARK/KILL verdict
/brainstorm [topic]	Creative brainstorming — cross-field connections, assumption challenges, alternative framings, wild cards
/triage-project	Project triage — should you continue, pivot, or kill? 5-signal assessment
/scooping-check [topic]	Scooping risk — who else is working on this? Watch list with researchers, venues, search terms

Core Pipeline

Command	Description
/write-paper [title]	Full pipeline — Phase 0 (evaluation) + Phases 1-8, start to finish
/search-papers [topic]	Phase 1: systematic literature search across 4 APIs
/draft-section [section]	Phase 4: write one specific section as complete paragraphs
/export-latex	Phase 6: convert finished draft to arxiv-style LaTeX + compiled PDF
/verify-citations	Phase 7: verify all citations against actual source content
/respond-reviewers [pdf or comments]	Phase 8: full revision loop — extract feedback, classify, implement, recompile, latexdiff
/generate-figure [description]	AI-generated academic diagram from text
/generate-plot [datafile] [intent]	Statistical plot from CSV/JSON data

Qualitative Data Analysis (v6.2.0)

Command	Description
/analyze-interviews [topic]	Qualitative analysis pipeline — structured summaries, thematic coding, cross-case analysis, evidence tables. Context-window-safe: summary-first, never loads all transcripts at once. Supports Gioia, Mayring, Braun & Clarke.

Extended Capabilities (v6.0.0)

Command	Description
/review-paper [venue]	Simulated peer review — 2 independent reviewer reports calibrated to top IS/CS venues
/screen-papers [criteria]	PRISMA-compliant SLR screening with quality assessment and flow diagram
/analyze-positioning	Differentiation matrix against closest related work + positioning statement
/analyze-writing [section]	Writing style analysis — passive voice, hedging, readability, 8 quality metrics
/prepare-submission [venue]	Venue-specific submission package: anonymization, cover letter, reviewer suggestions
/monitor-literature	Re-run search queries, find papers published since last search
/generate-slides [format]	Conference presentation slides with speaker notes (Marp-compatible)

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Architecture

Skill Engines

The plugin contains 16 specialized skill engines (~6,500 lines of domain knowledge) that the paper-machine agent orchestrates, plus 24 curated scientific skills that auto-activate by context:

Idea Evaluation Engine (v6.3.0)

Engine	Responsibility	Key Capabilities
idea-engine	Research idea evaluation	7-dimension scoring, conclusion-first test, RS1-RS8 principles, PURSUE/REFINE/KILL verdicts, cross-session persistence

Core Pipeline Engines

Engine	Responsibility	Key Capabilities
literature-engine	Systematic literature discovery	4 academic APIs, snowballing, PRISMA screening, concept matrix, monitoring
theory-engine	Theoretical framing	Theory matching, gap formulation, hypothesis/design principle derivation
method-engine	Research design	13 method templates (SLR, DSR, case study, Gioia, Mayring, grounded theory, PLS-SEM, mixed, experiment/RCT, action research, ethnography, Delphi, simulation) + research data management
writing-engine	Paragraph-level text production	Section templates, sentence formulas, academic register for IS/WI/BWL, style analysis (8 metrics)
qualitative-engine	Qualitative data analysis	Summary-first transcript processing, thematic coding (Gioia/Mayring/Braun & Clarke), cross-case analysis, evidence tables
figure-engine	Visual production	PaperBanana AI diagrams (Gemini) via direct Python API, matplotlib/seaborn fallback
latex-engine	Document compilation	arxiv-style conversion, \citep/\citet citation resolution, PDF build
verification-engine	Citation verification	Source retrieval (abstract + full-text), claim-source comparison, verification report
review-engine	Revision automation	PDF annotation extraction, comment classification, change planning, latexdiff generation

Extended-Capability Engines (v6.0.0)

Engine	Responsibility	Key Capabilities
screening-engine	SLR paper screening	PRISMA-compliant title/abstract + full-text screening, quality assessment, flow diagram
peer-review-engine	Simulated peer review	2 independent reviewer reports (Methodologist + Theorist), venue-calibrated, feeds into review-engine
positioning-engine	Competitive positioning	Differentiation matrix, unique positioning analysis, draft positioning paragraph
submission-engine	Submission preparation	Anonymization checks, cover letter generation, reviewer suggestions, venue formatting validation
presentation-engine	Slide generation	Conference slides with speaker notes from paper, Marp-compatible output
coauthor-engine	Author management	CRediT contribution tracking, human-AI division of labor documentation

MCP Servers (Bundled)

The academic-search MCP server is declared in plugin.json and starts automatically. PaperBanana is called via direct Python API (not MCP):

Component	Package	APIs	Purpose
academic-search (MCP)	academic-search-mcp	Semantic Scholar, OpenAlex, CrossRef, arXiv	Literature search, snowballing, multi-query, BibTeX/CSV export
PaperBanana (direct API)	paperbanana[google]	Google Gemini	AI diagram generation, statistical plots, diagram evaluation

Pipeline Agent

The plugin includes 4 agents:

Agent	File	Purpose
paper-machine	agents/paper-machine.md	Autonomous pipeline orchestrator — gates at Phase 0, then orchestrates all 16 skill engines through Phases 1-8
idea-critic	agents/idea-critic.md	Adversarial idea stress-test — 7 evaluation dimensions, PURSUE/REFINE/KILL verdicts
research-strategist	agents/research-strategist.md	Strategic advisor — 5 modes: project triage, comparative advantage, impact forecasting, opportunity cost, scooping risk
brainstormer	agents/brainstormer.md	Creative idea generator — cross-field connections, assumption challenges, alternative framings

The paper-machine agent orchestrates all 16 skill engines through the pipeline phases. Since v6.3.0, it evaluates ideas (Phase 0) before committing to production, using the three specialist agents. Operating principles:

1. Evaluate before producing. Phase 0 gates the pipeline (v6.3.0).
2. DO, don't ask. Make decisions and present results.
3. Produce text, not plans. Every phase yields deliverable output.
4. Checkpoint, don't block. Present work, then continue.
5. Be explicit about decisions. State what was chosen and why.
6. Save everything to files. Every phase produces artifacts.

Scientific Skills Library (v6.1.0)

Version 6.1.0 integrates 24 curated scientific skills from K-Dense AI's claude-scientific-skills (MIT License). These skills auto-activate based on context and complement the 15 core engines with domain-specific expertise:

Category	Skills	Complements
Research Ideation	hypothesis-generation, scientific-brainstorming, scientific-critical-thinking, consciousness-council, what-if-oracle	theory-engine
Writing	scientific-writing, citation-management, markdown-mermaid-writing	writing-engine
Literature & Review	literature-review, peer-review, scholar-evaluation	literature/review-engine
Statistics	statistical-analysis, exploratory-data-analysis, statsmodels	method-engine
Visualization	matplotlib, seaborn, plotly, scientific-visualization, scientific-schematics	figure-engine
Submission	venue-templates, research-grants	submission/latex-engine
Reference Management	pyzotero	verification-engine
Utility	get-available-resources, networkx	general

Skills are stored in scientific-skills/ and follow the Agent Skills standard. Each skill is a SKILL.md file with YAML frontmatter and structured domain knowledge. Claude Code discovers and loads them automatically when relevant to the current task.

Markdown-to-LaTeX Converter

scripts/md_to_latex.py (732 lines) converts the draft into production-ready LaTeX:

• (Author, Year) citations to \citep{key} / \citet{key} with BibTeX key matching
• Markdown tables to booktabs LaTeX tables
• Markdown images to LaTeX figure environments
• Section hierarchy mapping (# to \section, ## to \subsection, etc.)
• Abstract and keyword extraction
• [CITE], [DATA], [TODO] markers to LaTeX comments

# Standalone usage
python scripts/md_to_latex.py draft.md references.bib --output paper.tex --compile

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Output Files

After /write-paper + /export-latex, your project directory contains:

File	Description
draft.md	Complete paper draft with all sections
references.bib	BibTeX entries for all cited papers
literature_base.csv	Full literature database from search phase
concept_matrix.md	Webster & Watson concept matrix
framing.md	Theoretical framing (gap, RQs, contribution)
paper_structure.md	Paper structure with word budget
figures/	Generated diagrams and plots (PNG, 300 DPI)
status_report.md	Completion status and open items
latex/paper.tex	arxiv-style LaTeX source
latex/paper.pdf	Compiled PDF, ready for submission
verification_report.md	Citation verification results (after /verify-citations)
latex/paper_diff.pdf	Visual change tracking via latexdiff (after /respond-reviewers)
orchestration_log.md	Audit trail: timestamps, actors, decisions, quality gate outcomes (v5.2.0+)
outputs/revision_log_rN.md	Detailed change log per revision round (after /respond-reviewers)
interviews/summaries/	Structured interview summaries (after /analyze-interviews)
outputs/codebook_v1.md	First-order codes with definitions and examples (after /analyze-interviews)
outputs/theme_map.md	Second-order themes grouped from codes (after /analyze-interviews)
outputs/cross_case_analysis.md	Cross-interview comparison matrix (after /analyze-interviews)
outputs/evidence_table.md

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Repository Structure

.
├── .claude-plugin/
│   ├── plugin.json             # Plugin manifest + MCP server config
│   └── marketplace.json        # Marketplace definition for install
├── agents/                      # 4 agents (v6.3.0)
│   ├── paper-machine.md        # Autonomous pipeline agent (Phase 0-8)
│   ├── idea-critic.md          # Adversarial idea stress-test (7 dimensions)
│   ├── research-strategist.md  # Strategic research advisor (5 modes)
│   └── brainstormer.md         # Creative idea generator (cross-field)
├── commands/                    # 20 slash commands
│   ├── evaluate-idea.md        # /evaluate-idea → full idea stress-test (v6.3.0)
│   ├── brainstorm.md           # /brainstorm → creative idea generation (v6.3.0)
│   ├── triage-project.md       # /triage-project → continue/pivot/kill (v6.3.0)
│   ├── scooping-check.md       # /scooping-check → competition assessment (v6.3.0)
│   ├── write-paper.md          # /write-paper  → full pipeline
│   ├── search-papers.md        # /search-papers → Phase 1
│   ├── screen-papers.md        # /screen-papers → SLR screening
│   ├── draft-section.md        # /draft-section → Phase 4
│   ├── export-latex.md         # /export-latex  → Phase 6
│   ├── verify-citations.md     # /verify-citations → Phase 7
│   ├── respond-reviewers.md    # /respond-reviewers → Phase 8
│   ├── review-paper.md         # /review-paper → simulated peer review
│   ├── analyze-interviews.md   # /analyze-interviews → qualitative analysis (v6.2.0)
│   ├── analyze-positioning.md  # /analyze-positioning
│   ├── analyze-writing.md      # /analyze-writing → style analysis
│   ├── monitor-literature.md   # /monitor-literature
│   ├── prepare-submission.md   # /prepare-submission
│   ├── generate-figure.md      # /generate-figure
│   ├── generate-plot.md        # /generate-plot
│   └── generate-slides.md      # /generate-slides
├── principles/                  # Research strategy reference (v6.3.0)
│   └── research-strategy.md    # RS1-RS8 principles (Carlini + research-companion)
├── skills/                      # 16 domain-specific engines (~6,500 lines)
│   ├── idea-engine/            # Idea evaluation + Phase 0 orchestration (v6.3.0)
│   ├── literature-engine/      # Systematic literature search + monitoring
│   ├── theory-engine/          # Theoretical framing
│   ├── method-engine/          # 13 research method templates + RDM
│   ├── writing-engine/         # Academic writing templates + style analysis
│   ├── qualitative-engine/    # Qualitative data analysis (v6.2.0)
│   ├── figure-engine/          # Figure generation (PaperBanana)
│   ├── latex-engine/           # LaTeX conversion + compilation
│   ├── verification-engine/    # Citation verification against sources
│   ├── review-engine/          # Revision automation (PDF → implement → latexdiff)
│   ├── screening-engine/       # PRISMA-compliant SLR screening
│   ├── peer-review-engine/     # Simulated double-blind peer review
│   ├── positioning-engine/     # Competitive positioning analysis
│   ├── submission-engine/      # Venue-specific submission preparation
│   ├── presentation-engine/    # Conference slide generation
│   └── coauthor-engine/        # CRediT author contribution tracking
├── scientific-skills/              # 24 curated skills from K-Dense AI (v6.1.0)
│   ├── hypothesis-generation/     # Structured hypothesis formulation
│   ├── scientific-writing/        # IMRAD, reporting guidelines, APA/AMA
│   ├── statistical-analysis/      # Test selection, APA reporting
│   ├── matplotlib/                # Publication-quality plots
│   ├── venue-templates/           # LaTeX templates for 30+ venues
│   ├── consciousness-council/     # Multi-perspective deliberation
│   └── ... (24 skills total)
├── scripts/
│   ├── md_to_latex.py          # Markdown-to-LaTeX converter (732 lines)
│   ├── extract_annotations.py  # PDF annotation extraction (PyMuPDF)
│   ├── process_interviews.py   # Interview transcript processing (v6.2.0)
│   └── paperbanana_direct.py   # Direct PaperBanana API (bypasses MCP transport)
├── templates/
│   └── arxiv.sty               # arxiv-style LaTeX template
├── paper/                       # Technical paper (Blask, 2026)
│   ├── paper.tex               # LaTeX source
│   ├── paper.pdf               # Compiled PDF
│   ├── references.bib          # Bibliography
│   └── figures/                # PaperBanana-generated diagrams
├── .env.example                # API key template
├── .gitignore
├── LICENSE
└── README.md

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Figure Generation

PaperBanana implements the multi-agent pipeline from Zhu et al. (2026), powered by Google Gemini:

Retriever → Planner → Stylist → Visualizer → Critic (×3 iterations) → Final PNG

The 5-agent, 2-phase architecture uses in-context learning with curated reference examples (Phase 1: planning) and iterative VLM-as-Judge refinement (Phase 2: generation).

Direct Python API (v6.2.0)

Since v6.2.0, the figure-engine calls PaperBanana's Python API directly via asyncio.run(). Since v6.3.0, the MCP transport has been removed entirely — it was unreliable (timeouts, silent failures in long-running generation pipelines of ~35-45 seconds per figure).

Reliability chain: Direct Python API → matplotlib/seaborn (fallback).

The wrapper script scripts/paperbanana_direct.py supports all PaperBanana features:

# Generate a methodology diagram
python3 scripts/paperbanana_direct.py diagram \
  --source-context "Our method uses a transformer encoder..." \
  --caption "Figure 1: System Architecture" \
  --output-dir figures/ \
  --filename "fig_architecture.png" \
  --iterations 3 \
  --aspect-ratio 16:9

# Generate a statistical plot
python3 scripts/paperbanana_direct.py plot \
  --data '{"model": ["GPT-4", "Claude", "Gemini"], "accuracy": [0.89, 0.92, 0.87]}' \
  --caption "Model comparison on benchmark" \
  --output-dir figures/

# Download expanded reference set (294 examples, ~257 MB, one-time)
python3 scripts/paperbanana_direct.py download-references

API key loading priority: GOOGLE_API_KEY environment variable → ~/.paperbanana.env → project .env.

Environment	Engine	Quality
Claude Code	PaperBanana + Gemini (direct API)	AI-generated, publication-quality
Cowork	matplotlib / seaborn	Clean statistical plots

If PaperBanana is unavailable (no API key, network issues), the figure-engine falls back to Python-based generation using matplotlib and seaborn with academic styling presets.

References: Official research repo · Community MCP implementation · arXiv:2601.23265

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Citation Verification

The verification-engine checks whether cited papers actually support the claims attributed to them:

/verify-citations

How it works:

1. Extracts all \citep{}/\citet{} citations from paper.tex (or (Author, Year) from draft.md) with surrounding context
2. Matches to BibTeX entries and DOIs in references.bib
3. Fetches source material via academic-search MCP:
   • Tier A: Abstracts + TLDRs from Semantic Scholar, OpenAlex, CrossRef (always)
   • Tier B: Full-text PDFs for open-access papers — arXiv, DOAJ, etc. (when available)
4. Compares each attributed claim against actual source content
5. Classifies as: VERIFIED, PLAUSIBLE, MISMATCH, UNVERIFIABLE, or NOT FOUND
6. Generates verification_report.md with priority issues and fix recommendations

Prioritized processing: Load-bearing citations (gap statement, key statistics) are verified first, then methodology/theory citations, then background references. Results after each tier so you can act on critical mismatches immediately.

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Revision Automation (Phase 8)

The review-engine automates the co-author/reviewer revision loop — derived from 4 actual revision rounds on the paper that built this tool:

/respond-reviewers @annotated_review.pdf

How it works:

📄 Annotated PDF ──→ EXTRACT ──→ MAP ──→ CLASSIFY ──→ PLAN (Quality Gate)
                                                            │
                                              IMPLEMENT ──→ VERIFY ──→ DOCUMENT
                                                                         │
                     New Round ◄─────────────────────────────────────────┘

1. EXTRACT — Parses PDF annotations (highlights, sticky notes, strikeouts) via PyMuPDF, or parses pasted reviewer comments
2. MAP — Locates each comment's corresponding position in paper.tex (line number, section, context)
3. CLASSIFY — Determines action type (DELETE, REPLACE, MOVE, RESTRUCTURE, FIX, FIGURE, SHORTEN, APPROVE, QUESTION) and priority
4. PLAN — Presents a structured change plan table for your approval (quality gate — nothing executes without your OK)
5. IMPLEMENT — Executes changes in dependency order, invoking figure-engine or writing-engine as needed
6. VERIFY — Recompiles LaTeX (0 errors target), generates latexdiff for visual change tracking
7. DOCUMENT — Creates change log, optional R&R letter, updates orchestration log, commits

Supports: Co-author annotated PDFs, journal R&R decision letters, self-review output. Handles multilingual comments (German/English). Repeats for Round 1 → 2 → 3 → ... until acceptance.

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Supported Research Methods

The method-engine (797 lines) provides complete section templates for 13 methodologies:

• Systematic Literature Review (SLR) — PRISMA flow, inclusion/exclusion criteria
• Case Study — Yin methodology, cross-case analysis
• Gioia Method — 1st/2nd order coding, aggregate dimensions
• Mayring Content Analysis — Category system, coding rules
• Grounded Theory — Open/axial/selective coding
• PLS-SEM — Measurement model, structural model
• Design Science Research (DSR) — Hevner framework, evaluation cycles
• Mixed Methods — Sequential/concurrent designs
• Experiment / RCT — Experimental design, control groups, randomization
• Action Research — Iterative cycles, practitioner-researcher collaboration
• Ethnography — Fieldwork, participant observation, thick description
• Delphi Study — Expert panel, multi-round consensus building
• Simulation — Agent-based modeling, discrete event simulation

Plus Research Data Management guidance: FAIR principles, data management plans (DMP), data availability statements.

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Troubleshooting

Problem	Solution
paperbanana: command not found	Run pip install paperbanana[mcp,google]
academic-search-mcp: command not found	Run pip install academic-search-mcp
GOOGLE_API_KEY not set	Create ~/.paperbanana.env with your key (see Installation)
API key expired / 400 error	Get a new key at Google AI Studio and update ~/.paperbanana.env
PaperBanana figure generation times out	Ensure you're on v6.3.0+ — the unreliable MCP transport has been removed. Figures are now generated via direct Python API only
/write-paper not recognized	Plugin not loaded. Run /plugin install open-academic-paper-machine@open-paper-machine
LaTeX compilation fails	Install LaTeX: brew install --cask mactex-no-gui (macOS) or apt install texlive-full (Linux)
No module named 'paperbanana'	Ensure you installed with extras: pip install paperbanana[mcp,google] (not just pip install paperbanana)

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AI Usage Disclaimer

Many journals and conferences now require disclosure of AI tool usage in research (ACM Policy, Springer Nature Policy, Elsevier Policy). Below is a template disclaimer you can adapt for papers produced with or assisted by this tool.

Short Version (permissive venues)

Use of AI tools. The authors used the Open Academic Paper Machine (Blask, 2026) for [literature search / text drafting / figure generation / LaTeX conversion / citation verification]. All output was reviewed and revised by the authors, who take full responsibility for the final content.

Full Version (strict venues: ACM, Nature, Elsevier)

Use of AI-assisted tools. This paper was produced with the assistance of the Open Academic Paper Machine (Blask, 2026), a Claude Code plugin that automates academic paper production workflows. Specifically, AI was used for: [select applicable] (1) systematic literature search and deduplication across academic databases; (2) drafting initial text for [specific sections, e.g., "the Related Work section"]; (3) generation of publication-quality figures using PaperBanana (Zhu et al., 2026); (4) conversion to LaTeX and bibliography management; (5) citation verification against source abstracts. All AI-generated content was reviewed, validated, and revised by the author(s). The author(s) take full responsibility for the final content, including the accuracy of all claims, citations, and conclusions. The tool and its documentation are openly available at https://github.com/TobiasBlask/open-paper-machine.

Guidance

• Be specific. Don't just write "AI was used." State which tasks were AI-assisted and which parts you wrote or verified yourself.
• Claim responsibility. Journals want to know a human checked the output. Make that explicit.
• Cite the tools. Reference both the Paper Machine and PaperBanana if you used AI figures (see Citation below).
• Check venue policy. Requirements vary — some venues require the disclaimer in a footnote, others in an acknowledgments section, others in a cover letter. Check your target venue's AI policy before submission.
• Not an author. Current consensus across major publishers: AI tools cannot be listed as co-authors. Credit them in the methods or acknowledgments instead.

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Citation

If you use this tool in your research, please cite the technical paper:

@article{blask2026opm,
  title={The Open Academic Paper Machine: An Autonomous {LLM} Plugin for
         End-to-End Academic Paper Production},
  author={Blask, Tobias-Benedikt},
  year={2026},
  note={Working paper}
}

The position paper (orchestrated through human-AI interaction using this system):

@article{blask2026creator,
  title={From Creator to Orchestrator? How an {LLM} Agent Wrote This Paper
         and What That Means for Science},
  author={Blask, Tobias-Benedikt and Funk, Burkhardt},
  journal={SSRN Electronic Journal},
  year={2026},
  note={Available at \url{https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6358578}}
}

The idea evaluation framework (v6.3.0) is inspired by:

• Nicholas Carlini's essay How to Win a Best Paper Award (2026) — conclusion-first test, taste for problems, kill conditions, unreasonable effort
• Andre Huang's research-companion plugin (MIT License) — 3 agents, 7 evaluation dimensions, RS1-RS8 principles

The figure generation pipeline is based on:

@article{zhu2026paperbanana,
  title={PaperBanana: Automating Academic Illustration for {AI} Scientists},
  author={Zhu, Dawei and Meng, Rui and Song, Yale and Wei, Xiyu and Li, Sujian
          and Pfister, Tomas and Yoon, Jinsung},
  journal={arXiv preprint arXiv:2601.23265},
  year={2026}
}

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License

MIT License. See LICENSE for details.

Author: Prof. Dr. Tobias-Benedikt Blask — Harz University of Applied Sciences