Paper to code
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.From its SKILL.md
npx -y skills add dongzhigang13305312738-art/paper-skills --skill paper-to-codeAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
3 things to look at
- 22 days oldThe repository was created 22 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
3.2 KB, 628 tokens by cl100k_base, as published. Nobody here has run it
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:
- 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
What ships with it: 1 file
8.8 KB alongside SKILL.md
references/
- paper-to-code-prompts.md8.8 KB