Paper To Code Skill for Claude
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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/lingzhi227/agent-research-skills --skill paper-to-codeThe instruction itself
12 sections, as written by the authorPaper 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:
- Overall Plan: Extract methodology, experiments, datasets, hyperparameters, evaluation metrics
- Architecture Design: Generate file list, Mermaid classDiagram, sequenceDiagram
- Task Breakdown: Logic analysis per file, dependency-ordered task list, required packages
- Configuration: Extract training details into
config.yaml
Stage 2: Analysis
For each file in the task list (dependency order):
- Conduct detailed logic analysis
- Map paper methodology to code structure
- Reference the config.yaml for all settings
- Follow the UML class diagram interfaces strictly
Stage 3: Coding
For each file in dependency order:
- Generate code with access to all previously generated files
- Follow the design's data structures and interfaces exactly
- Reference config.yaml — never fabricate configuration values
- Write complete code — no TODOs or placeholders
Stage 4: Debugging (if needed)
If execution fails:
- Collect error messages
- Identify root cause using SEARCH/REPLACE diff format
- Apply minimal fixes preserving original intent
- 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
- Downstream: experiment-code
- See also: code-debugging, algorithm-design
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How to use it
Take lingzhi227/paper-to-code from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.