PaperTool/.opencode/skills/paper-parsing/SKILL.md

name	description
paper-parsing	Use when analyzing ML/DL papers to ensure comprehensive extraction of all relevant information

Paper Parsing Methodology

Overview

Systematic approach to parsing ML/DL papers for replication. Emphasizes completeness and openness to avoid missing critical details.

Announce at start: "I'm using the paper-parsing skill to ensure comprehensive paper analysis."

Core Philosophy

1. Completeness over speed: Better to extract too much than miss something
2. Open-ended discovery: Papers contain unique insights; don't force into templates
3. Cross-reference: Information appears in multiple places; cross-check
4. Explicit uncertainty: Mark unclear items rather than guessing

Paper Sections Checklist

Abstract

• Core contribution identified
• Key results/numbers extracted
• Problem domain understood

Introduction

• Problem motivation clear
• Gap in existing work identified
• Proposed solution summarized
• Claimed contributions listed

Related Work

• Key prior methods identified
• Differences from this work noted
• Potential baselines for comparison

Method / Approach

• Architecture fully described
• All components identified
• Mathematical formulation complete
• Training procedure detailed
• Loss functions specified
• Hyperparameters listed

Experiments

• Datasets listed with sizes
• Evaluation metrics defined
• Baseline comparisons noted
• Ablation studies cataloged
• Key numerical results extracted

Appendix / Supplementary

• Additional implementation details
• Extended results
• Proofs or derivations
• Code references

Information Extraction Patterns

Architecture Details

Look for:

• Layer types and configurations
• Activation functions
• Normalization methods
• Attention mechanisms
• Skip connections
• Input/output dimensions

Common locations:

• Method section figures
• Architecture diagrams
• Table of hyperparameters
• Appendix implementation details

Training Configuration

Parameter	Typical Locations
Learning rate	Experiments, Appendix
Batch size	Experiments, Appendix
Optimizer	Method, Appendix
Epochs	Experiments
Hardware	Experiments, Appendix
Training time	Experiments

Numerical Results

Extract from:

• Main results tables
• Comparison figures
• Ablation tables
• Training curves (approximate values)

Format as:

Metric	Dataset	Value	Conditions
Accuracy	CIFAR-10	95.2%	ResNet-50 backbone

Common Omissions to Watch For

1. Initialization: Often in appendix or not mentioned
2. Data augmentation: May be standard but unspecified
3. Early stopping criteria: Often implied
4. Evaluation protocol: Train/val/test split details
5. Random seeds: Reproducibility details
6. Software versions: PyTorch, CUDA versions

Quality Verification

Before completing analysis:

1. Coverage check: Every section reviewed?
2. Consistency check: Numbers match across sections?
3. Completeness check: Could someone implement from this?
4. Ambiguity check: Unclear items marked?

Output Quality Markers

Good analysis:

• Specific numbers, not "good performance"
• Exact layer configs, not "standard ResNet"
• Explicit uncertainty markers
• Cross-references between sections

Poor analysis:

• Vague descriptions
• Missing hyperparameters
• No numerical targets
• Assumptions without noting them

Red Flags

If you notice:

• "Implementation details in code" → Check GitHub link
• "Standard settings" → Look up the standard
• "Following [citation]" → May need to read that paper
• Inconsistent numbers → Note the discrepancy