/inno-paper-reviewer
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), ML/CS conference review forms (NeurIPS/ICML/ICLR/ACL
Claude Code
$ npx -y skills add OpenLAIR/dr-claw --skill inno-paper-reviewer --agent claude-code
Codex
$ npx skills add OpenLAIR/dr-claw --skill inno-paper-reviewer -a codex -y
Cursor
$ npx skills add OpenLAIR/dr-claw --skill inno-paper-reviewer -a cursor -y
Gemini CLI
$ npx skills add OpenLAIR/dr-claw --skill inno-paper-reviewer -a gemini-cli -y
GitHub Copilot
$ npx skills add OpenLAIR/dr-claw --skill inno-paper-reviewer -a github-copilot -y
OpenCode
$ npx skills add OpenLAIR/dr-claw --skill inno-paper-reviewer -a opencode -y
Windsurf
$ npx skills add OpenLAIR/dr-claw --skill inno-paper-reviewer -a windsurf -y
SKILL.md
inno-paper-reviewer.SKILL.mdname: inno-paper-reviewer
description: Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), ML/CS conference review forms (NeurIPS/ICML/ICLR/ACL style), and constructive feedback. Best for actual review writing and manuscript revision. To evaluate a research idea rather than a manuscript, use inno-idea-eval.
allowed-tools: Read Write Edit Bash
license: MIT license
metadata:
skill-author: K-Dense Inc.Scientific Critical Evaluation and Peer Review
Overview
Peer review is a systematic process for evaluating scientific manuscripts. Assess methodology, statistics, design, reproducibility, ethics, and reporting standards. Apply this skill for manuscript and grant review across disciplines with constructive, rigorous evaluation.
When to Use This Skill
This skill should be used when:
- Conducting peer review of scientific manuscripts for journals
- Evaluating grant proposals and research applications
- Assessing methodology and experimental design rigor
- Reviewing statistical analyses and reporting standards
- Evaluating reproducibility and data availability
- Checking compliance with reporting guidelines (CONSORT, STROBE, PRISMA)
- Writing a conference-style review (scores, strengths, weaknesses, questions) for an ML/CS paper
- Providing constructive feedback on scientific writing
---
Peer Review Workflow
Conduct peer review systematically through the following stages, adapting depth and focus based on the manuscript type and discipline.
Stage 1: Initial Assessment
Begin with a high-level evaluation to determine the manuscript's scope, novelty, and overall quality.
**Key Questions:**
- What is the central research question or hypothesis?
- What are the main findings and conclusions?
- Is the work scientifically sound and significant?
- Is the work appropriate for the intended venue?
- Are there any immediate major flaws that would preclude publication?
**Output:** Brief summary (2-3 sentences) capturing the manuscript's essence and initial impression.
Stage 2: Detailed Section-by-Section Review
Conduct a thorough evaluation of each manuscript section, documenting specific concerns and strengths.
Abstract and Title
- **Accuracy:** Does the abstract accurately reflect the study's content and conclusions?
- **Clarity:** Is the title specific, accurate, and informative?
- **Completeness:** Are key findings and methods summarized appropriately?
- **Accessibility:** Is the abstract comprehensible to a broad scientific audience?
Introduction
- **Context:** Is the background information adequate and current?
- **Rationale:** Is the research question clearly motivated and justified?
- **Novelty:** Is the work's originality and significance clearly articulated?
- **Literature:** Are relevant prior studies appropriately cited?
- **Objectives:** Are research aims/hypotheses clearly stated?
Methods
- **Reproducibility:** Can another researcher replicate the study from the description provided?
- **Rigor:** Are the methods appropriate for addressing the research questions?
- **Detail:** Are protocols, reagents, equipment, and parameters sufficiently described?
- **Ethics:** Are ethical approvals, consent, and data handling properly documented?
- **Statistics:** Are statistical methods appropriate, clearly described, and justified?
- **Validation:** Are controls, replicates, and validation approaches adequate?
**Critical elements to verify:**
- Sample sizes and power calculations
- Randomization and blinding procedures
- Inclusion/exclusion criteria
- Data collection protocols
- Computational methods and software versions
- Statistical tests and correction for multiple comparisons
Results
- **Presentation:** Are results presented logically and clearly?
- **Figures/Tables:** Are visualizations appropriate, clear, and properly labeled?
- **Statistics:** Are statistical results properly reported (effect sizes, confidence intervals, p-values)?
- **Objectivity:** Are results presented without over-interpretation?
- **Completeness:** Are all relevant results included, including negative results?
- **Reproducibility:** Are raw data or summary statistics provided?
**Common issues to identify:**
- Selective reporting of results
- Inappropriate statistical tests
- Missing error bars or measures of variability
- Over-fitting or circular analysis
- Batch effects or confounding variables
- Missing controls or validation experiments
Discussion
- **Interpretation:** Are conclusions supported by the data?
- **Limitations:** Are study limitations acknowledged and discussed?
- **Context:** Are findings placed appropriately within existing literature?
- **Speculation:** Is speculation clearly distinguished from data-supported conclusions?
- **Significance:** Are implications and importance clearly articulated?
- **Future directions:** Are next steps or unanswered questions discussed?
**Red flags:**
- Overstated conclusions
- Ignoring contradictory evidence
- Causal claims from correlational data
- Inadequate discussion of limitations
- Mechanistic claims without mechanistic evidence
References
- **Completeness:** Are key relevant papers cited?
- **Currency:** Are recent important studies included?
- **Balance:** Are contrary viewpoints appropriately cited?
- **Accuracy:** Are citations accurate and appropriate?
- **Self-citation:** Is there excessive or inappropriate self-citation?
Stage 3: Methodological and Statistical Rigor
Evaluate the technical quality and rigor of the research with particular attention to common pitfalls.
**Statistical Assessment:**
- Are statistical assumptions met (normality, independence, homoscedasticity)?
- Are effect sizes reported alongside p-values?
- Is multiple testing correction applied appropriately?
- Are confidence intervals provided
Read more
name: inno-paper-reviewer
description: Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), ML/CS conference review forms (NeurIPS/ICML/ICLR/ACL style), and constructive feedback. Best for actual review writing and manuscript revision. To evaluate a research idea rather than a manuscript, use inno-idea-eval.
allowed-tools: Read Write Edit Bash
license: MIT license
metadata:
skill-author: K-Dense Inc.Scientific Critical Evaluation and Peer Review
Overview
Peer review is a systematic process for evaluating scientific manuscripts. Assess methodology, statistics, design, reproducibility, ethics, and reporting standards. Apply this skill for manuscript and grant review across disciplines with constructive, rigorous evaluation.
When to Use This Skill
This skill should be used when:
- Conducting peer review of scientific manuscripts for journals
- Evaluating grant proposals and research applications
- Assessing methodology and experimental design rigor
- Reviewing statistical analyses and reporting standards
- Evaluating reproducibility and data availability
- Checking compliance with reporting guidelines (CONSORT, STROBE, PRISMA)
- Writing a conference-style review (scores, strengths, weaknesses, questions) for an ML/CS paper
- Providing constructive feedback on scientific writing
---
Peer Review Workflow
Conduct peer review systematically through the following stages, adapting depth and focus based on the manuscript type and discipline.
Stage 1: Initial Assessment
Begin with a high-level evaluation to determine the manuscript's scope, novelty, and overall quality.
**Key Questions:**
- What is the central research question or hypothesis?
- What are the main findings and conclusions?
- Is the work scientifically sound and significant?
- Is the work appropriate for the intended venue?
- Are there any immediate major flaws that would preclude publication?
**Output:** Brief summary (2-3 sentences) capturing the manuscript's essence and initial impression.
Stage 2: Detailed Section-by-Section Review
Conduct a thorough evaluation of each manuscript section, documenting specific concerns and strengths.
Abstract and Title
- **Accuracy:** Does the abstract accurately reflect the study's content and conclusions?
- **Clarity:** Is the title specific, accurate, and informative?
- **Completeness:** Are key findings and methods summarized appropriately?
- **Accessibility:** Is the abstract comprehensible to a broad scientific audience?
Introduction
- **Context:** Is the background information adequate and current?
- **Rationale:** Is the research question clearly motivated and justified?
- **Novelty:** Is the work's originality and significance clearly articulated?
- **Literature:** Are relevant prior studies appropriately cited?
- **Objectives:** Are research aims/hypotheses clearly stated?
Methods
- **Reproducibility:** Can another researcher replicate the study from the description provided?
- **Rigor:** Are the methods appropriate for addressing the research questions?
- **Detail:** Are protocols, reagents, equipment, and parameters sufficiently described?
- **Ethics:** Are ethical approvals, consent, and data handling properly documented?
- **Statistics:** Are statistical methods appropriate, clearly described, and justified?
- **Validation:** Are controls, replicates, and validation approaches adequate?
**Critical elements to verify:**
- Sample sizes and power calculations
- Randomization and blinding procedures
- Inclusion/exclusion criteria
- Data collection protocols
- Computational methods and software versions
- Statistical tests and correction for multiple comparisons
Results
- **Presentation:** Are results presented logically and clearly?
- **Figures/Tables:** Are visualizations appropriate, clear, and properly labeled?
- **Statistics:** Are statistical results properly reported (effect sizes, confidence intervals, p-values)?
- **Objectivity:** Are results presented without over-interpretation?
- **Completeness:** Are all relevant results included, including negative results?
- **Reproducibility:** Are raw data or summary statistics provided?
**Common issues to identify:**
- Selective reporting of results
- Inappropriate statistical tests
- Missing error bars or measures of variability
- Over-fitting or circular analysis
- Batch effects or confounding variables
- Missing controls or validation experiments
Discussion
- **Interpretation:** Are conclusions supported by the data?
- **Limitations:** Are study limitations acknowledged and discussed?
- **Context:** Are findings placed appropriately within existing literature?
- **Speculation:** Is speculation clearly distinguished from data-supported conclusions?
- **Significance:** Are implications and importance clearly articulated?
- **Future directions:** Are next steps or unanswered questions discussed?
**Red flags:**
- Overstated conclusions
- Ignoring contradictory evidence
- Causal claims from correlational data
- Inadequate discussion of limitations
- Mechanistic claims without mechanistic evidence
References
- **Completeness:** Are key relevant papers cited?
- **Currency:** Are recent important studies included?
- **Balance:** Are contrary viewpoints appropriately cited?
- **Accuracy:** Are citations accurate and appropriate?
- **Self-citation:** Is there excessive or inappropriate self-citation?
Stage 3: Methodological and Statistical Rigor
Evaluate the technical quality and rigor of the research with particular attention to common pitfalls.
**Statistical Assessment:**
- Are statistical assumptions met (normality, independence, homoscedasticity)?
- Are effect sizes reported alongside p-values?
- Is multiple testing correction applied appropriately?
- Are confidence intervals provided
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Questions about inno-paper-reviewer.
- Which coding agents can use inno-paper-reviewer?
- inno-paper-reviewer installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

