steering

name

steering

description

Run activation-steering feature discovery for a Hugging Face model. Use this skill whenever the user says /steering, wants to steer a model, extract a cognitive feature, generate steering vectors, run feature discovery, or produce steering artifacts. Specify a model (default: gpt2) and an optional feature name. If a feature is supplied, generate inputs, expected outputs, then run an extraction pass and output artifacts. If no feature is supplied, auto-pick one that hasn't already been extracted. If no test data is supplied, generate synthetic examples; otherwise use the user's data and fill in whatever's missing.

/steering

Run activation-steering feature discovery end-to-end: specify a model and an optional feature, and this skill orchestrates data generation, vector extraction, and artifact output.

When to use this skill

• The user types /steering or asks to "steer" a model
• The user wants to extract a cognitive feature (chain_of_thought, few_shot_prompting, react, etc.)
• The user wants to generate steering artifacts for a model
• The user asks to run feature discovery or build a steering vector
• The user mentions activation engineering, representation engineering, or contrastive extraction

How it works

Inputs

Parameter	Default	Description
model_name	"gpt2"	Any decoder-only Hugging Face model identifier
feature_name	auto-pick	A feature to extract (e.g. chain_of_thought). If omitted, the system picks the next undiscovered feature from the standard catalog.
user_examples	generate	Optional list of FeatureExample objects with text and label ("positive" / "negative"). If omitted, synthetic examples are generated. If only one label is present, the other is generated.
layer_idx	5	Transformer layer for hidden-state collection
output_dir	None	Directory to write plugin artifacts to

Pipeline

1. Resolve feature — look up in the standard catalog, or generate a new spec.
2. Ensure data — use user-supplied examples, fill missing labels with synthetic data, or generate all data.
3. Load model — load_model_and_tokenizer(model_name).
4. Discover vectors — discover_feature_vectors(feature_spec, layer_idx, model, tokenizer, device).
5. Write artifacts — write_artifact_plugin(...) to produce a distributable plugin bundle.

Output

A SteeringResult with:

• feature_spec — the resolved FeatureSpec
• discovered_vectors — list of DiscoveredFeatureVector (torch tensors + metadata)
• artifact_dir — path to the written plugin directory (if output_dir was set)

Quick-start

from activation_steering.steering_command import SteeringRunConfig, run_steering

# Explicit feature
result = run_steering(SteeringRunConfig(
    model_name="gpt2",
    feature_name="chain_of_thought",
    output_dir="./artifacts",
))

# Auto-pick next undiscovered feature
result = run_steering(SteeringRunConfig(
    model_name="gpt2",
    output_dir="./artifacts",
))

# With user-supplied data
from activation_steering.features import FeatureExample
result = run_steering(SteeringRunConfig(
    model_name="gpt2",
    feature_name="my_custom_feature",
    user_examples=[
        FeatureExample(text="detailed reasoning before answer", label="positive"),
        FeatureExample(text="just the answer", label="negative"),
    ],
    output_dir="./artifacts",
))

Artifacts produced

<output_dir>/<model_name>/<feature_name>/
├── plugin.json          # Manifest
├── feature_specs.json   # The resolved FeatureSpec
└── controllers.json     # Discovered steering vectors

Standard catalog features (gpt2)

Feature	Category
few_shot_prompting	prompt_engineering
retrieval_augmented_context	context_engineering
react	cognitive_architecture
chain_of_thought	reasoning_strategy

Implementation

The command is implemented in activation_steering/steering_command.py and exported from activation_steering:

• SteeringRunConfig — run configuration dataclass
• SteeringResult — run output dataclass
• run_steering(config) — main orchestrator
• build_steering_feature_spec(...) — resolve or generate a FeatureSpec
• pick_undiscovered_feature(...) — auto-select next feature
• generate_synthetic_examples(...) — create training data

API reference

For implementation details, read activation_steering/steering_command.py.