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AmitabhainArunachala
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mi-experimenter

Automate mechanistic interpretability experiments with R_V measurement and causal validation.

1|Updated Feb 14, 2026
Search Tags:#mechanistic interpretability#activation patching#R_V#causal validation#transformer analysis#LLM interpretability
One-click install
npx skills add https://github.com/AmitabhainArunachala/clawd --skill mi-experimenter
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mi-experimenter
Source: https://github.com/AmitabhainArunachala/clawd/tree/main/skills/mi-experimenter
Command: npx skills add https://github.com/AmitabhainArunachala/clawd --skill mi-experimenter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, numpy, pandas, scipy, transformers, accelerate, rv-toolkit, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates complex mechanistic interpretability experiments, enabling researchers to understand how specific components of neural networks contribute to their behavior.

Core Features & Use Cases

  • R_V Measurement: Quantify the representational quality of activations using the R_V metric.
  • Causal Validation: Run controlled experiments to isolate the causal impact of specific model components (e.g., layers, attention heads) on behavior.
  • Cross-Architecture Analysis: Compare R_V across different model families (GPT-2, Llama, Mistral) to find generalizable patterns.
  • Use Case: Identify which MLP layers in a large language model are most critical for understanding factual recall by ablating them and measuring the resulting drop in R_V.

Quick Start

Use the mi-experimenter skill to run causal validation on the 'mistralai/Mistral-7B-v0.1' model targeting layer 27.

Frequently Asked Questions about mi-experimenter

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

FAQPage Schema
How do I perform causal validation on transformer attention heads?▼

Causal validation on transformer attention heads is performed using controlled activation patching to isolate and measure the specific impact of model components on behavior. This Skill automates running those ablation experiments and statistical tests.

What is the R_V metric for LLM interpretability?▼

The R_V metric in LLM interpretability quantifies the representational quality of neural network activations. This Skill automates R_V measurement to help researchers assess how specific components contribute to overall model behavior.

Can I compare mechanistic interpretability results across different LLM architectures?▼

Yes, you can compare mechanistic interpretability results across different LLM architectures. This Skill facilitates cross-architecture analysis, allowing you to measure and compare R_V metrics across model families like GPT-2, Llama, and Mistral.

How do I identify critical MLP layers for factual recall using ablation studies?▼

To identify critical MLP layers for factual recall, you run ablation studies by targeting specific layers and measuring the resulting drop in R_V. This Skill automates layer necessity assessment to pinpoint the most critical components.

Does this mechanistic interpretability tool require specific Python dependencies?▼

This mechanistic interpretability tool requires specific Python dependencies including torch, numpy, pandas, scipy, transformers, accelerate, and the rv-toolkit to function properly. You must install these packages before running experiments.

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