ai-character-stability

Compute controllability and predictability metrics to classify AI-character stability.

5|1|Updated Mar 20, 2026

Search Tags:#risk-assessment #control-theory #llm-safety #character-analysis #ai-character-stability #stability-margin

Ready to run

npx skills add https://github.com/gpsnmeajp/ai-character-checker --skill ai-character-stability

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-character-stability
Source: https://github.com/gpsnmeajp/ai-character-checker/tree/main/skills/ai-character-stability
Command: npx skills add https://github.com/gpsnmeajp/ai-character-checker --skill ai-character-stability

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill diagnoses AI-character stability by evaluating controllability and predictability to reduce undesired behaviors in long-running interactions and complex prompts.

Core Features & Use Cases

Assess controllability and predictability of AI-character configurations.
Generate actionable stabilization recommendations and safety guardrails for chat, storytelling, and agent-style roles.
Use in scenarios such as multi-turn conversations, prompt-driven tasks, and role-based simulations to compare different character designs.

Quick Start

Provide a character setup and run the stability analysis to produce Cx, Ac, C, P, and SM scores.

Frequently Asked Questions about ai-character-stability

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

FAQPage Schema

How do I assess AI character stability in long-running conversations?▼

To assess AI character stability in long-running conversations, you evaluate controllability and predictability. This analysis computes metrics like Cx, Ac, C, P, and SM to quantify stability across multi-turn dialogue and prompt-driven tasks.

What is control theory application in LLM safety and character analysis?▼

Control theory in LLM safety character analysis identifies and quantifies how controllability and predictability influence AI-character stability. It classifies characters into four quadrants to outline practical stabilization recommendations and safety guardrails.

How do I calculate stability margin for AI character configurations?▼

You calculate stability margin (SM) for AI character configurations by running a stability analysis on the character setup. This generates specific metrics including Cx, Ac, C, P, and SM to evaluate risk and predict undesired behaviors.

Can I use character analysis for prompt-driven tasks and role-based simulations?▼

Yes, you can use character analysis for prompt-driven tasks and role-based simulations. The analysis applies across various scenarios including dialogue-heavy interactions, long-term conversations, and agent-style roles to compare different character designs.

Does AI character stability analysis work without external dependencies?▼

AI character stability analysis works without external dependencies. It directly evaluates your character setup to diagnose stability issues and generate actionable stabilization recommendations based on computed metrics.

Why does my AI character exhibit undesired behaviors in complex prompts?▼

Your AI character exhibits undesired behaviors in complex prompts due to low controllability and predictability. Evaluating these factors and classifying the character into stability quadrants helps outline practical guardrails to reduce these issues.