Use when a completed research manuscript needs a robust internal, venue-calibrated mock peer review. Orchestrates three isolated reviewer subagents in parallel, then a separate MetaReview subagent that verifies evidence, reports agreement and score dispersion, and emits advisory reroutes. Never revises research artifacts, executes experiments, or updates pipeline state.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
The required runtime workflow ingests the manuscript text/pages (e.g., `paper/main.pdf` → `scripts/prepare_pdf_review.py` → `paper/reviews/<venue>-review-r<N>/pdf-intake/document.txt` and per-page text/render) and LLMs then read that content to produce reviews.
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