paper2code

arxiv URL in → citation-anchored implementation out

┌─────────────────────────────┐         ┌──────────────────────────────────────┐
│                             │         │  {paper_slug}/                       │
│  /paper2code                │         │  ├── README.md                       │
│  https://arxiv.org/abs/     │  ───▶   │  ├── REPRODUCTION_NOTES.md          │
│  1706.03762                 │         │  ├── requirements.txt               │
│                             │         │  ├── src/                            │
│                             │         │  │   ├── model.py     # §3.2 cited  │
│                             │         │  │   ├── loss.py      # §3.4 cited  │
│                             │         │  │   ├── train.py     # §4.1 cited  │
│                             │         │  │   ├── data.py                    │
│                             │         │  │   ├── evaluate.py                │
│                             │         │  │   └── utils.py                   │
│                             │         │  ├── configs/                        │
│                             │         │  │   └── base.yaml   # all params   │
│                             │         │  └── notebooks/                      │
│                             │         │      └── walkthrough.ipynb           │
└─────────────────────────────┘         └──────────────────────────────────────┘

[placeholder: animated GIF showing the full pipeline — paper fetch → parsing → ambiguity audit → code generation → walkthrough notebook]

Why this exists

The problem: ML papers are vague. Critical hyperparameters are buried in appendices or omitted entirely. Prose contradicts equations. "Standard settings" refers to nothing specific. When you implement a paper, you spend more time detective-working than coding.

What LLMs get wrong: Naive code generation fills in every gap silently and confidently. You get something that runs but doesn't match the paper. Worse, you can't tell which parts are from the paper and which were invented by the model.

What paper2code does differently:

1. Citation anchoring — every line of generated code references the exact paper section and equation it implements (§3.2, Eq. 4)
2. Ambiguity auditing — before writing a single line of code, every implementation choice is classified as SPECIFIED, PARTIALLY_SPECIFIED, or UNSPECIFIED
3. Honest uncertainty — unspecified choices are flagged with [UNSPECIFIED] comments at the exact line where the choice is made, with common alternatives listed
4. Appendix mining — appendices, footnotes, and figure captions are treated as first-class sources, not ignored

The result: code you can trust because you can verify every decision against the paper.

Install

npx skills add PrathamLearnsToCode/paper2code/skills/paper2code

You'll be prompted to:

1. Select agents — pick the coding agents you want to use this skill with (e.g., Claude Code)
2. Choose scope — Global (recommended) or project-level
3. Choose method — Symlink (recommended) or copy

Once installed, open your agent and run the skill:

claude  # or your preferred agent

Usage

Basic — generate a minimal implementation

/paper2code https://arxiv.org/abs/1706.03762

Specify framework

/paper2code https://arxiv.org/abs/2006.11239 --framework jax

Full mode — includes training loop and data pipeline

/paper2code 2106.09685 --mode full

Educational mode — extra comments and pedagogical notebook

/paper2code https://arxiv.org/abs/2010.11929 --mode educational

Using bare arxiv ID

/paper2code 1706.03762

What you get

attention_is_all_you_need/
├── README.md                    # Paper summary, contribution statement, quick-start
├── REPRODUCTION_NOTES.md        # Ambiguity audit, unspecified choices, known deviations
├── requirements.txt             # Pinned dependencies
├── src/
│   ├── model.py                 # Architecture — every layer cited to paper section
│   ├── loss.py                  # Loss functions with equation references
│   ├── data.py                  # Dataset class skeleton with preprocessing TODOs
│   ├── train.py                 # Training loop (if in scope)
│   ├── evaluate.py              # Metric computation code
│   └── utils.py                 # Shared utilities (masking, positional encoding, etc.)
├── configs/
│   └── base.yaml                # All hyperparams — each one cited or flagged [UNSPECIFIED]
└── notebooks/
    └── walkthrough.ipynb        # Pedagogical notebook linking paper sections → code → sanity checks

Key files explained

What this skill will NOT do

• Won't guarantee correctness. The implementation matches what the paper describes. If the paper is wrong, the code is wrong. If the paper is vague, the code flags it.
• Won't invent details. If the paper doesn't specify a hyperparameter, the code uses a common default and marks it [UNSPECIFIED]. It will never silently fill in gaps.
• Won't download datasets. The data.py provides a Dataset class skeleton with clear instructions on where to get the data and how to preprocess it.
• Won't set up training infrastructure. No distributed training, no experiment tracking, no checkpointing beyond what the paper's contribution requires.
• Won't implement baselines. Only the core contribution of the paper is implemented.
• Won't reimplement standard components. If the paper says "standard transformer encoder," the code imports it or notes the dependency — it doesn't reimplement attention from scratch.

Design principles

Citation anchoring convention

Every non-trivial code decision is anchored to the paper:

# §3.2 — "We apply layer normalization before each sub-layer" (Pre-LN variant)
class TransformerBlock(nn.Module):
    def forward(self, x):
        # §3.2, Eq. 2 — attention_weights = softmax(QK^T / sqrt(d_k))
        attn_out = self.attention(self.norm1(x))  # (batch, seq_len, d_model)
        x = x + attn_out  # §3.2 — residual connection

The UNSPECIFIED flag system

# [UNSPECIFIED] Paper does not state epsilon for LayerNorm — using 1e-6 (common default)
# Alternatives: 1e-5 (PyTorch default), 1e-8 (some implementations)
self.norm = nn.LayerNorm(d_model, eps=1e-6)

# [ASSUMPTION] Using pre-norm based on "we found pre-norm more stable" in §4.1
# The paper uses post-norm in Figure 1 but pre-norm in experiments — ambiguous

Ambiguity classification

Contributing

Adding worked examples

Worked examples are the most trust-building part of this project. To add one:

1. Pick a well-known paper (people should be able to verify the output)
2. Run the skill: /paper2code https://arxiv.org/abs/XXXX.XXXXX
3. Save the full output to skills/paper2code/worked/{paper_slug}/
4. Write a review.md that honestly evaluates:
   • What the skill got right
   • What it correctly flagged as unspecified
   • Any mistakes it made
   • Any edge cases it handled well or poorly
5. Submit a PR with all of the above

Improving guardrails

If you find a pattern where the skill hallucinates or makes a silent assumption, add it to the appropriate file in guardrails/.

Adding domain knowledge

If papers in your subfield consistently reference components that the skill doesn't know about (e.g., graph neural network primitives, RL components), add a knowledge file in knowledge/.

Worked examples

This repo includes fully worked examples to demonstrate output quality:

Each includes the complete generated output plus an honest review.md evaluating what the skill got right and wrong.