feat(skills): add paper-parsing skill

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

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+---
+name: paper-parsing
+description: 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