/paper-to-code

name: paper-to-code
description: Convert an ML research paper into a complete, runnable code repository. 3-stage pipeline from Paper2Code — Planning (UML + dependency graph) → Analysis (per-file logic) → Coding (dependency-ordered generation). Use for reproducing paper methods.
argument-hint: [paper-pdf-or-text]

Paper to Code

Convert a research paper into a complete, runnable code repository.

Input

• `$0` — Paper PDF path, paper text, or paper URL

References

• Paper2Code prompts (planning, analysis, coding stages): `~/.claude/skills/paper-to-code/references/paper-to-code-prompts.md`

Workflow (from Paper2Code)

Stage 1: Planning

Four-turn conversation to create a comprehensive plan:

1. **Overall Plan**: Extract methodology, experiments, datasets, hyperparameters, evaluation metrics 2. **Architecture Design**: Generate file list, Mermaid classDiagram, sequenceDiagram 3. **Task Breakdown**: Logic analysis per file, dependency-ordered task list, required packages 4. **Configuration**: Extract training details into `config.yaml`

Stage 2: Analysis

For each file in the task list (dependency order): 1. Conduct detailed logic analysis 2. Map paper methodology to code structure 3. Reference the config.yaml for all settings 4. Follow the UML class diagram interfaces strictly

Stage 3: Coding

For each file in dependency order: 1. Generate code with access to all previously generated files 2. Follow the design's data structures and interfaces exactly 3. Reference config.yaml — never fabricate configuration values 4. Write complete code — no TODOs or placeholders

Stage 4: Debugging (if needed)

If execution fails: 1. Collect error messages 2. Identify root cause using SEARCH/REPLACE diff format 3. Apply minimal fixes preserving original intent 4. Re-run until successful

Output Structure

reproduced_code/
├── config.yaml        # Training configuration
├── main.py            # Entry point
├── model.py           # Model architecture
├── dataset_loader.py  # Data loading
├── trainer.py         # Training loop
├── evaluation.py      # Metrics and evaluation
├── reproduce.sh       # Run script
└── requirements.txt   # Dependencies

Key Constraints

• **Dependency order**: Each file is generated with access to all previously generated files
• **Interface contracts**: Mermaid diagrams serve as rigid interface definitions across all stages
• **No fabrication**: Only use configurations explicitly stated in the paper
• **Complete code**: Every function must be fully implemented

Rules

• Follow the paper's methodology exactly — do not invent improvements
• Generate code in dependency order (data loading → model → training → evaluation → main)
• Use config.yaml for all hyperparameters and settings
• Every class/method in UML diagram must exist in code
• Generate a reproduce.sh script for one-command execution
• If paper details are ambiguous, note them explicitly

Related Skills

• Upstream: [literature-search](../literature-search/)
• Downstream: [experiment-code](../experiment-code/)
• See also: [code-debugging](../code-debugging/), [algorithm-design](../algorithm-design/)