How do I install the Saelens skill?

Run npx skillmds@latest add zjunlp/saelens in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Saelens safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Saelens?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Saelens free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Saelens?

zjunlp (@zjunlp) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Saelens by zjunlp — Agent Skill

Name: Saelens
Author: zjunlp

Saelens

Use this skill when working with Sparse Autoencoders (SAEs) for mechanistic interpretability of language models, including training SAEs, loading pre-trained SAEs, analyzing neural network features, or integrating SAEs with TransformerLens, HuggingFace Transformers, or other PyTorch-based models.

zjunlp 64c2347 2 files · 3.1 KB Updated Sep 17, 2026

File contents

SAELens — Sparse Autoencoders for Language Models

When to Use

Activate this skill when:

Training sparse autoencoders (SAEs) on language model activations
Loading and analyzing pre-trained SAEs from Neuronpedia or HuggingFace
Performing mechanistic interpretability research on transformer models
Investigating neural network features using SAE decompositions
Generating SAE feature dashboards with SAE-Vis
Hooking SAEs into transformer forward passes (HookedSAETransformer)
Evaluating SAE quality (variance explained, L0 sparsity, reconstruction loss)
Running cache activation pipelines for large-scale SAE training
Analyzing logit lens features through SAE decomposition
Working with TopK, Gated, or Standard SAE architectures

Keywords: sparse autoencoder, SAE, mechanistic interpretability, TransformerLens, feature analysis, neural network features, activation patching, SAE training, HookedSAETransformer, Neuronpedia, dictionary learning, GPT-2, language model interpretability

Installation / Setup

Prerequisites

Python 3.10+
PyTorch (CUDA recommended for training)

Install from PyPI (recommended)

pip install sae-lens

Install from source (development)

git clone https://github.com/decoderesearch/SAELens.git
cd SAELens
pip install -e ".[dev]"

Quick Reference

Documentation: https://decoderesearch.github.io/SAELens/
PyPI Package: https://pypi.org/project/sae-lens/
GitHub: https://github.com/decoderesearch/SAELens
Pre-trained SAEs list: https://decoderesearch.github.io/SAELens/latest/pretrained_saes/
Migration guide (v6): https://decoderesearch.github.io/SAELens/latest/migrating/
Neuronpedia: https://www.neuronpedia.org/
SAE-Vis Library: https://github.com/callummcdougall/sae_vis
SAEBench: https://github.com/adamkarvonen/SAEBench
Colab Tutorials:

Optional dependencies

---name: saelensdescription: Use this skill when working with Sparse Autoencoders (SAEs) for mechanistic interpretability of language models, including training SAEs, loading pre-trained SAEs, analyzing neural network features, or integrating SAEs with TransformerLens, HuggingFace Transformers, or other PyTorch-based models.---# SAELens — Sparse Autoencoders for Language Models## When to UseActivate this skill when:- Training sparse autoencoders (SAEs) on language model activations- Loading and analyzing pre-trained SAEs from Neuronpedia or HuggingFace- Performing mechanistic interpretability research on transformer models- Investigating neural network features using SAE decompositions- Generating SAE feature dashboards with SAE-Vis- Hooking SAEs into transformer forward passes (HookedSAETransformer)- Evaluating SAE quality (variance explained, L0 sparsity, reconstruction loss)- Running cache activation pipelines for large-scale SAE training- Analyzing logit lens features through SAE decomposition- Working with TopK, Gated, or Standard SAE architectures**Keywords:** sparse autoencoder, SAE, mechanistic interpretability, TransformerLens, feature analysis, neural network features, activation patching, SAE training, HookedSAETransformer, Neuronpedia, dictionary learning, GPT-2, language model interpretability---## Installation / Setup### Prerequisites- Python 3.10+- PyTorch (CUDA recommended for training)### Install from PyPI (recommended)```bashpip install sae-lens```### Install from source (development)```bashgit clone https://github.com/decoderesearch/SAELens.gitcd SAELenspip install -e ".[dev]"```## Quick Reference- **Documentation:** https://decoderesearch.github.io/SAELens/- **PyPI Package:** https://pypi.org/project/sae-lens/- **GitHub:** https://github.com/decoderesearch/SAELens- **Pre-trained SAEs list:** https://decoderesearch.github.io/SAELens/latest/pretrained_saes/- **Migration guide (v6):** https://decoderesearch.github.io/SAELens/latest/migrating/- **Neuronpedia:** https://www.neuronpedia.org/- **SAE-Vis Library:** https://github.com/callummcdougall/sae_vis- **SAEBench:** https://github.com/adamkarvonen/SAEBench- **Colab Tutorials:** - [SAE Lens + Neuronpedia](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/tutorial_2_0.ipynb) - [Loading and Analysing Pre-Trained SAEs](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb) - [Logit Lens with Features](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/logits_lens_with_features.ipynb) - [Training a Sparse Autoencoder](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/training_a_sparse_autoencoder.ipynb) - [Training SAEs on Synthetic Data](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/training_saes_on_synthetic_data.ipynb) - [SynthSAEBench](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/synth_sae_bench.ipynb)---### Optional dependencies

Frequently asked questions