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taylorsatulataylorsatulaCommunity·1 Agent Skills Included

TeaLeaves

Visualize and measure how models attend to prompts

Captures how a language model distributes attention across every region of a prompt at every layer. Generates heatmaps, cooking curves, animated layer sweeps, and logit lens projections for any HuggingFace model. Replaces guesswork-based prompt tuning with measurable before-and-after comparisons and multi-seed stability checks. Runs analysis on remote GPU boxes and produces comparison reports locally.
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npx skills add taylorsatula/TeaLeaves --all -g -y
Available:

Instructs the agent to act as a mechanistic interpretability researcher that interprets attention data, validates prompt changes against cooking curves and logit lens metrics, and follows the pipeline's technical constraints when running experiments.

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taylorsatulataylorsatula

MI Analysis Operational Reference

Automate MI analysis of prompts with region annotation, attention capture, and logit-lens projections.

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Frequently Asked Questions

FAQPage Schema
How to install TeaLeaves?▼

Run `npx skills add taylorsatula/TeaLeaves --all -g -y` in your terminal to install the skill globally.

What does TeaLeaves measure?▼

It captures attention weights at every layer of a model's forward pass, showing exactly which regions of your prompt the model focuses on and when it commits to output tokens.

How does TeaLeaves improve prompt engineering?▼

Instead of eyeballing outputs, you compare attention heatmaps and cooking curves before and after each prompt change to see measurably what improved or degraded.

Which models does TeaLeaves support?▼

It works with any HuggingFace transformer model, auto-discovering the architecture, layer count, and attention modules from the model config.

Do I need a GPU to use TeaLeaves?▼

Yes for the attention capture step, which runs on a remote GPU box such as Vast.ai; rendering and analysis of results run locally on any machine.

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