shiken

Construct paired test cases to evaluate AI agent reasoning at divergence points.

1|Updated Apr 18, 2026

Search Tags:#testing #validation #evaluation #AI #reasoning #self-assessment

Ready to run

npx skills add https://github.com/ntholm86/autonomous-agent-skills --skill shiken

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shiken
Source: https://github.com/ntholm86/autonomous-agent-skills/tree/main/archive/v2/shiken
Command: npx skills add https://github.com/ntholm86/autonomous-agent-skills --skill shiken

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps identify whether an AI agent is truly reasoning or merely pattern-matching by constructing and analyzing deliberate examination scenarios.

Core Features & Use Cases

Designs targeted probes to test the agent's reasoning capabilities under novel or complex conditions.
Creates paired test cases that share surface features but differ in key details to reveal reasoning divergence.
Analyzes reasoning trails at predicted divergence points to assess the agent's situational understanding and genuine reasoning skills.
Use Case: Test whether an AI correctly interprets nuanced differences in scenarios where routines fail but interpretive reasoning should succeed.

Quick Start

Provide a pair of similar cases with expected divergence points to evaluate the agent's reasoning behavior in complex decision-making situations.

Frequently Asked Questions about shiken

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

FAQPage Schema

How do I test if an AI agent is genuinely reasoning instead of pattern matching?▼

To test if an AI agent is genuinely reasoning, construct targeted examination probes using paired cases with shared surface features but divergent underlying details to reveal true understanding versus routine pattern-matching.

How do I evaluate an AI model's situational understanding under novel conditions?▼

Evaluate AI model situational understanding by designing complex assessment scenarios where standard routines fail, then analyze reasoning trails at predicted divergence points to validate interpretive reasoning capabilities.

What is the best way to validate AI reasoning capabilities using paired test cases?▼

The best way to validate AI reasoning capabilities is providing paired test cases with clear divergence points, analyzing whether the agent correctly interprets nuanced differences where pattern-based responses fail but true reasoning succeeds.

When do I need to construct targeted probes for AI self-assessment and validation?▼

Construct targeted probes for AI self-assessment when you need to distinguish genuine reasoning from pattern-matching in complex decision-making tasks, specifically when evaluating interpretive reasoning under novel or complex conditions.

Does AI reasoning evaluation work without designing scenarios with divergence points?▼

AI reasoning evaluation requires designing scenarios with clear divergence points to analyze reasoning trails effectively, as paired cases sharing surface features but differing in key details are essential to reveal true reasoning behavior.

Why does an AI agent fail to interpret nuanced differences in similar scenarios?▼

An AI agent fails to interpret nuanced differences when it relies on pattern-matching rather than genuine reasoning, which targeted probes reveal by creating paired cases where routines fail but interpretive reasoning should succeed.