model-steering

Apply steering vectors and persistent edits to transformer activations during generation.

9|2|Updated Jan 7, 2026
One-click install
npx skills add https://github.com/ndif-team/skills --skill model-steering
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: model-steering
Source: https://github.com/ndif-team/skills/tree/main/plugins/nnsight/skills/model-steering
Command: npx skills add https://github.com/ndif-team/skills --skill model-steering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Model steering enables controlling outputs without retraining by applying steering vectors and persistent edits to activations.

Core Features & Use Cases

  • Persistent model editing across traces
  • Real-time activation steering
  • Multi-layer steering
  • Activation-based probing

Quick Start

Apply a steering vector to a selected layer to bias the next generated outputs toward a desired direction.

Frequently Asked Questions about model-steering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is activation steering for language models?

Activation steering controls model outputs without retraining by applying steering vectors to transformer activations. It modifies the residual stream in real time to bias generated text toward a desired direction during inference.

How do I apply steering vectors to transformer layers?

You apply steering vectors by accessing layer outputs and residual streams during tracing, then injecting persistent edits into selected layers. This influences generated text in a single pass or across iterative generation.

Can I maintain persistent model edits across multiple inference sessions?

Persistent edits can be maintained across traces and sessions by applying steering vectors to the residual stream. This preserves the modified model state for continuous, iterative generation without retraining.

Does activation steering require access to internal transformer layer outputs?

Activation steering requires access to internal layer outputs, residual streams, and an interface to apply temporary or persistent edits during tracing and generation. This access is necessary to influence model behavior dynamically.

What is the best way to edit a neural network's behavior without retraining?

Applying steering vectors to transformer activations is the best way to edit neural network behavior without retraining. It enables real-time, multi-layer steering to control outputs during single-pass inference.

When should I not use steering vectors for model editing?

Steering vectors are not suitable when you lack access to layer outputs or residual streams. If the model interface prevents applying temporary or persistent edits during tracing, activation steering will not function.