add-model

name	add-model
description	Add a new pathology foundation model to the README (excluding magnification). Use when the user asks to add a model, paper, or feature extractor to the list, or mentions a new paper in the papers/ directory.

Add Model to README

Add a new pathology foundation model row to README.md by extracting information from its paper PDF and associated GitHub repository. This skill covers all fields except magnification, which is handled separately by the add-magnification skill.

Prerequisites

First, follow the read-paper skill (.claude/skills/read-paper/SKILL.md) to find and thoroughly read the paper. Then return here to add the model row.

Workflow

Step 1: Determine the Table

The README has two tables:

Table	Criteria
Patch-level models	Produces patch/tile embeddings (most models)
Slide-level / patient-level models	Produces WSI-level or patient-level embeddings without supervision

Step 2: Extract All Fields

For patch-level models, extract these fields:

Field	Description	Notes
Name	Model name with paper link	Use **bold** if >100K WSIs
Group	Research group/institution	Link to lab website if available
Weights	:white_check_mark: or :x:	Check GitHub/HuggingFace
Released	Date + link to first release	Format: Mon YYYY[*](link)
SSL	Self-supervised learning method	Link to paper if novel method
WSIs	Number of whole-slide images	Use **bold** if >100K; round to 2 sig figs
Tiles	Number of patches/tiles	Round to 2 sig figs
Patients	Number of patients/cases	Leave blank if not reported
Batch size	Training batch size	Leave blank if not reported
Iterations	Training iterations or epochs	Use "X epochs" or "XK" for iterations
Architecture	Model architecture	e.g., ResNet-50, ViT-B, ViT-L
Parameters	Number of parameters	e.g., 86M, 632M
Embed dim	Output embedding dimension	e.g., 768, 1024, 2048
Input size	Input image size at inference time (in pixels)	Usually 224
Magnification	Leave blank	Will be added separately via add-magnification skill
Dataset	Training dataset names	e.g., TCGA, PAIP, proprietary
Links	GitHub and/or HuggingFace icons	See format below

For slide-level models, the fields differ slightly (no Tiles column, has Patch size instead).

Step 3: Format the Row

Use this format for links:

[<img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/github.svg" width="20">](GITHUB_URL)
[<img src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg" width="25">](HF_URL)

Mark vision-language models with (VL) after the name.

Step 4: Insert in Chronological Order

Models are ordered by release date. Find the correct position and insert the new row.

Step 5: Verify Accuracy

Cross-check extracted values:

1. Re-read relevant paper sections to confirm numbers
2. Verify GitHub README matches paper claims
3. Ensure batch size/epochs refer to main model training, not ablations
4. Check that patient counts are for pretraining data, not evaluation sets

Step 6: Document Inferred Values in NOTES.md

For values that required inference or calculation (not directly stated in paper/code), add a brief explanation to NOTES.md. This provides transparency for how difficult-to-find values were derived.

Only document values that:

• Were inferred from multiple sources
• Required combining information from different parts of the paper
• Involved checking upstream repositories or external sources

The order of models in NOTES.md should match their order in README.md (chronological by release date).

Keep notes concise—one bullet point per inferred value.

Step 7: Report Findings to User

After adding the row, provide the user with a detailed summary of your findings. For every column in the row, include:

1. Value: The value you added (or state that you couldn't find it)
2. Source quote: A direct quote from the PDF or GitHub that supports this value
3. Reasoning: Brief explanation of how you determined this value from the quote

Format each field like this:

**Field Name**: [value]
- Quote: "[exact quote from paper/GitHub]" (Section X.X / GitHub README)
- Reasoning: [explanation of how you derived the value from the quote]

If a value was not found, explain:

• What sections you checked
• Why the information appears to be unreported

If a value required inference or calculation (e.g., computing tiles from WSIs × tiles-per-WSI), show your work.

This transparency helps the user verify accuracy and catch any misinterpretations.

Note: Remind the user that magnification was left blank and can be added using the add-magnification skill.

Example Row

| [ModelName](https://paper-url) | [Lab Name](https://lab-url) | :white_check_mark: | Jan 2024[\*](https://arxiv.org/abs/XXXX.XXXXXv1) | DINOv2 | 50K | 100M | 10K | 1024 | 100 epochs | ViT-B | 86M | 768 | 224 | | TCGA | [<img src="https://raw.githubusercontent.com/FortAwesome/Font-Awesome/6.x/svgs/brands/github.svg" width="20">](https://github.com/org/repo) |

Conventions

• Round WSIs, tiles, patients to 2 significant figures (e.g., 14,325,848 → 14M)
• Use K for thousands, M for millions, B for billions
• Bold model name and WSI count if trained on >100K slides
• Use INE suffix for "ImageNet epochs" if applicable
• Leave cells blank (not "N/A") for unreported values
• Add ** note in row if a value was inferred from other numbers