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saga-representation-analyzer

Analyze AI reasoning trajectories, isometry, and curvature to diagnose bottlenecks and predict performance without logprobs access.

Updated Aug 27, 2026
Search Tags:#prompt engineering#model optimization#ai diagnostics#representation analysis#reasoning quality#bottleneck identification
One-click install
npx skills add https://github.com/monkey1sai/jacks_happy_bots --skill saga-representation-analyzer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: saga-representation-analyzer
Source: https://github.com/monkey1sai/jacks_happy_bots/tree/main/workspace-cortex/skills/saga-representation-analyzer
Command: npx skills add https://github.com/monkey1sai/jacks_happy_bots --skill saga-representation-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of understanding and improving the internal reasoning quality of AI models, particularly when direct access to model internals like logprobs is unavailable.

Core Features & Use Cases

  • Analyze Reasoning Quality: Evaluates the "straightness" of thought trajectories, the consistency of semantic and reasoning distances, and the curvature of the model's internal representation space.
  • Diagnose Bottlenecks: Identifies specific steps or types of issues (knowledge, reasoning, attention, context) hindering AI performance.
  • Predict Performance: Estimates task accuracy based on analyzed representation metrics.
  • Use Case: When an AI agent's performance dips, this Skill can be used to diagnose whether the issue lies in the model's internal "thinking" process, providing actionable insights for prompt engineering or model fine-tuning.

Quick Start

Use the saga-representation-analyzer skill to diagnose the reasoning quality for the last 10 failed tasks.

Frequently Asked Questions about saga-representation-analyzer

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

FAQPage Schema
How do I diagnose AI reasoning bottlenecks without logprobs access?▼

AI model reasoning quality is evaluated by analyzing trajectory linearity, representational isometry, and spatial curvature. These metrics measure the straightness of thought trajectories and the consistency between semantic and reasoning distances to diagnose internal thinking issues.

How do I predict AI task accuracy based on internal representation metrics?▼

AI task accuracy is predicted by analyzing internal representation metrics such as trajectory linearity and representational isometry. This diagnostic process evaluates the model's internal reasoning quality to estimate performance outcomes and identify optimization opportunities.

Can I identify specific reasoning failures in an AI agent's failed tasks?▼

Specific reasoning failures in AI agents can be identified by analyzing the internal representation quality of recent failed tasks. This diagnostic process isolates bottlenecks related to knowledge, reasoning, attention, or context to guide prompt engineering or fine-tuning.

What is the best way to optimize prompt engineering using reasoning diagnostics?▼

Prompt engineering is optimized by using reasoning diagnostics to pinpoint exact bottlenecks in the AI's thinking process. Analyzing trajectory linearity and spatial curvature provides actionable insights for refining prompts to improve model performance.

When should I use representation analysis instead of direct model internals?▼

Representation analysis should be used when direct access to model internals like logprobs is unavailable. It employs proxy methods for confidence and stability estimation to evaluate reasoning quality and diagnose performance bottlenecks effectively.

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