sparse-autoencoder-training
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill saelens --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SAELens: Sparse Autoencoders for Mechanistic Interpretability SAELens is the primary library for training and analyzing Sparse Autoencoders (SAEs) - a technique for decomposing polysemantic neural network activations into sparse, interpretable features. Based on Anthropic's groundbreaking research on monosemanticity. **GitHub**: [jbloomAus/SAELens](https://github.com/jbloomAus/SAELens) (1,100+ stars) ## The Problem: Polysemanticity & Superposition Individual neurons in neural networks are **polysemantic** - they activate in multiple, semantically distinct contexts. This happens because models use **superposition** to represent more features than they have neurons, making interpretability difficult. **SAEs solve this** by decomposing dense activations into sparse, monosemantic features - typically only a small number of features activate for any given input, and each feature corresponds to an interpretable concept. ## When to Use SAELens **Use SAELens when you need to:** - Discover interpretable features in model activations - Understand what concepts a model has learned - Study superposition and feature geometry - Perform feature-based steering or ablation - Analyze safety-releva
- The Problem: Polysemanticity & Superposition
- When to Use SAELens
- Installation
- Core Concepts
- What SAEs Learn
- Key Validation (Anthropic Research)
- Workflow 1: Loading and Analyzing Pre-trained SAEs
- Step-by-Step
- Available Pre-trained SAEs
- Checklist
- Workflow 2: Training a Custom SAE
- Key Hyperparameters
- Evaluation Metrics
- Workflow 3: Feature Analysis and Steering
pip install sae-lens
What does the sparse-autoencoder-training skill do?
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill saelens --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.