peer-review-agent 0.1.0

Automated peer-review toolkit that extracts text, analyzes quality, and generates review artifacts.

Automated Peer Review AI Agent

An end-to-end automation toolkit that ingests research articles (PDF or DOCX), extracts text, performs heuristic quality checks, and produces a full peer-review packet for journal clubs or manuscript review boards.

Key Features

• Multiformat ingestion – reads both PDF and DOCX sources.
• Heuristic appraisal – detects study design, sample size, statistical methods, and flags missing elements (power statements, limitations, precision, etc.).
• Auto-generated collateral:
  • Markdown critical analysis report.
  • Presentation-ready PPTX deck.
  • Structured peer-review DOCX (major/minor comments).
  • Annotated text file with inline pseudo track changes.
  • Redline-style DOCX with prioritized action items and section-specific rewrite suggestions (works for PDFs by embedding extracted text).
• Batch orchestration – scan a root directory or target a single project folder.
• Extensible CLI – toggle peer-review artifacts, annotations, and redlines independently.

Installation & Distribution Options

1. Local development (recommended)

   git clone https://github.com/hssling/Automated_Peer_Review_AI_Agent.git
   cd Automated_Peer_Review_AI_Agent
   python -m venv .venv && .venv\Scripts\activate  # or source .venv/bin/activate on *nix
   pip install -e .
   

   Editable installs make iterating on heuristics and outputs straightforward.

2. Pip/Pipx distribution (implemented)
   The repository ships with a pyproject.toml; run pip install . to build a wheel. For isolated CLI usage, pipx install . creates a self-contained executable environment ideal for automation servers.

3. Containerization (suggested)
   For fully reproducible deployments (CI agents, on-prem review services), wrap the CLI in a lightweight Python container (e.g., Python 3.11-slim) and mount the articles directory. See “Future Enhancements” for ideas.

CLI Usage

After installation, the CLI peer-review-agent becomes available.

peer-review-agent --root "/path/to/root" [--peer-review] [--annotate] [--redline] [--force]

Common workflows:

• Single project folder

  peer-review-agent --folder "D:/Journal club/TB cohort study" --peer-review --annotate --redline
  

• Batch process every subfolder under a root directory

  peer-review-agent --root "/data/articles" --peer-review --annotate
  

Flags:

• --root PATH – scan root subfolders for PDFs/DOCXs.
• --folder PATH – process a specific folder (overrides --root discovery).
• --peer-review – create structured DOCX peer-review memo.
• --annotate – emit annotated text with inline pseudo comments.
• --redline – generate redline-style DOCX with action plan and rewrite suggestions (works for PDF inputs via extracted text).
• --force – rebuild outputs even if files already exist.

Output Artifacts

For each source article <stem> the agent produces (based on enabled flags):

• <stem>.txt – raw extracted text.
• <stem>_auto_critical_analysis.md – heuristic report with strengths/gaps.
• <stem>_auto_appraisal.pptx – slide deck summarizing findings.
• <stem>_peer_review.docx – major/minor comment log (when --peer-review).
• <stem>_annotated_comments.txt – inline comments (when --annotate).
• <stem>_redline_review.docx – prioritized action list + inline suggestions (when --redline).

Derived artifacts are automatically skipped during subsequent runs to avoid recursion.

Requirements

• Python 3.9+
• Dependencies: PyPDF2, python-docx, python-pptx

Install via pip/venv:

pip install -r requirements.txt

Continuous Integration

A GitHub Actions workflow (.github/workflows/ci.yml) runs linting and unit tests on every push/pull request using Python 3.11. Extend the pipeline with packaging/publishing steps once ready to distribute wheels or Docker images.

Tests

Unit tests live under tests/. Run:

pytest

Roadmap / Future Enhancements

• Publish pre-built wheels to PyPI for pip install peer-review-agent.
• Add optional Docker image for air-gapped deployments.
• Integrate citation parsing and GRADE-style scoring.
• Support configurable templates (custom PPT branding, peer-review rubrics).

Contributions and issue reports are welcome!