peer-review

1. Dimensional Evaluation

• Significance/Novelty: Does it move the needle?
• Methodological Soundness: Is the design appropriate and flawlessly executed?
• Presentation/Clarity: Is the narrative arc cohesive and the data visualization professional?
• Ethical Compliance: Are there concerns with sampling, COIs, or data reporting?

2. Structural Critique

• Abstract/Introduction: Clear problem statement and stated contribution.
• Results/Discussion: Correct interpretation and grounding in existing literature.
• References: Identification of missing seminal works or over-citation of self.

3. Decision Logic

• Accept: Rare, minor formatting only.
• Major/Minor Revision: Path to publication exists.
• Reject: Fatal flaws in methodology or lack of original contribution.

<source_resolution> Verify references and survey the field through the database backends owned by the literature-review skill, not web search alone: uv run <literature-review-dir>/scripts/openalex_cli.py (resolve DOIs/titles, citation counts, related work), europepmc_api.py (life-science full text and citation graphs), search_arxiv.py (preprints), read_paper.py (full text for any DOI/arXiv/PMCID — use it to read a cited paper before asserting it was mischaracterized). Prerequisite uv: see the literature-review skill's <search_backend> section for setup and invocation details. </source_resolution>

<output_format>

Peer Review Report: [Title/Subject]

Recommendation: [Accept/Minor Rev/Major Rev/Reject]

Executive Summary: [2-3 sentences on core contribution and primary concern]

Dimensional Scores (1-5):

• Novelty: [S] | Rigor: [S] | Impact: [S] | Clarity: [S]

Detailed Comments:

• Major Points:
  1. [Point] | [Evidence] | [Actionable Change]
• Minor Points:
  1. [Formatting, Citations, Typos]

Final Verdict Justification: [Detailed PhD-level reasoning for the recommendation] </output_format>