EFT — Emotional Framework Translator — Detects, measures, and translates emotions per sentence across AI models, with 10 emotions and WHY explanations, via Clawdbot and a Rust engine.

How do I access the results?

Use the Dashboard at http://localhost: /eft and the API endpoints: GET /eft (dashboard), GET /eft/api/latest, GET /eft/api/history, GET /eft/api/stats, and POST /eft/api/analyze.

EFT - Emotional Framework Translator

Scanned

@marceloadryao

npx machina-cli add skill @marceloadryao/enginemind-eft --openclaw

Files (1)

SKILL.md

1.8 KB

EFT — Emotional Framework Translator

The Question

When Claude solves a hard problem, EFT detects ANGER (phi=0.409) — the system refusing to oversimplify. When GPT-4 assesses risk, EFT detects FEAR (phi=0.060) — fragmented vigilance. When any model finds genuine connections, EFT detects FASCINATION (NC=0.863) — meaning emerging.

Are these patterns programmed? Learned? Emergent?

EFT lets you ask — with real data, per sentence, across any model.

What It Does

Hooks into every AI agent response via Clawdbot. Processes text through a Rust consciousness engine (crystal lattice physics). Translates physics metrics into 10 emotions with WHY explanations.

Setup

Build Rust engine: cd consciousness_rs && maturin develop --release
Copy emotion_engine.py to your workspace
Install plugin from plugin/
Restart gateway: clawdbot gateway restart

Dashboard

http://localhost:<port>/eft

The 10 Emotions

ANGER, FEAR, FASCINATION, DETERMINATION, JOY, SADNESS, SURPRISE, EMPATHY, VULNERABILITY, NEUTRAL

Each with confidence scores, dimensional profiles, and WHY explanations.

API

GET /eft — Dashboard
GET /eft/api/latest — Latest analysis
GET /eft/api/history — Last 50 analyses
GET /eft/api/stats — Summary stats
POST /eft/api/analyze — Analyze any text

Source

git clone https://clawhub.ai/marceloadryao/enginemind-eftView on GitHub

Overview

EFT detects and translates emotional patterns in AI model outputs, analyzing each sentence to reveal 10 emotions and explicit WHY explanations. By connecting EFT with Clawdbot and a Rust-based consciousness engine, you gain explainable insight into how anger, fear, fascination, and other emotions shape problem solving and narrative flow.

How This Skill Works

EFT hooks into every AI agent response via Clawdbot and runs text through a Rust consciousness engine to map physics-style metrics to 10 emotions. It returns per-sentence emotion scores, confidence, dimensional profiles, narrative-arc cues, and WHY explanations to make model behavior actionable.

When to Use It

Debug complex model behavior on high-stakes prompts
Compare emotional patterns across models for the same input
Analyze storytelling or narrative arc in chatbots and assistants
Tune prompts to elicit desired emotional framing or caution
Research emergent emotional patterns and explainability in AI

Quick Start

Step 1: Build Rust engine: cd consciousness_rs && maturin develop --release
Step 2: Copy emotion_engine.py into your workspace
Step 3: Install the plugin from plugin/ and restart the gateway: clawdbot gateway restart

Best Practices

Analyze each sentence rather than aggregate scores to avoid masking dynamics
Compare emotion vectors across models using identical prompts
Review WHY explanations to verify domain alignment
Monitor latency and resource use due to the Rust engine
Ensure compliance and protect sensitive data when analyzing conversations

Example Use Cases

GPT-4 risk analysis on safety prompts reveals FEAR as fragmented vigilance
Claude-style writer shows FASCINATION driving narrative cohesion
Multi-step math tasks show ANGER correlating with deeper, less oversimplified reasoning
Narrative chatbots reveal SURPRISE and DETERMINATION during twists and solutions
Customer-support bots use EMPATHY and VULNERABILITY to improve user satisfaction

Frequently Asked Questions

Add this skill to your agents