audit-loop-run

Audit long-horizon AI loop execution history and state transitions.

7|2|Updated Jan 3, 2026
One-click install
npx skills add https://github.com/BrennonTWilliams/little-loops --skill audit-loop-run
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: audit-loop-run
Source: https://github.com/BrennonTWilliams/little-loops/tree/main/skills/audit-loop-run
Command: npx skills add https://github.com/BrennonTWilliams/little-loops --skill audit-loop-run

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the challenge of verifying whether an AI-driven loop actually achieved its intended goal, helping to identify phantom successes, structural defects, and inefficient iterations.

Core Features & Use Cases

  • Goal Verification: Audits loop execution against stated goals, checking for artifact mutations and threshold contract satisfaction.
  • Fault Detection: Identifies specific failure signals like evaluate errors, throttle stops, and over-escaped shell corruption.
  • Improvement Proposals: Generates ranked, actionable recommendations to refine loop structure and evaluator logic.

Quick Start

Use the audit-loop-run skill to assess the effectiveness of the most recent execution of the loop named apo-textgrad.

Frequently Asked Questions about audit-loop-run

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

FAQPage Schema
How do I verify that an AI loop actually achieved its intended goal?▼

To verify AI loop effectiveness, you audit execution history and state transitions against stated goals, checking for artifact mutations and threshold contract satisfaction to detect phantom successes.

What is a phantom success in automated software development workflows?▼

A phantom success occurs when a long-horizon AI loop reports goal achievement without actually satisfying threshold contracts or artifact mutations, which auditing event logs can detect and flag.

How do I identify performance bottlenecks and structural failures in long-horizon AI loops?▼

Identify performance bottlenecks and structural failures in long-horizon AI loops by analyzing event logs, artifact mutations, and state transitions to detect evaluate errors, throttle stops, and shell corruption.

Can I generate improvement proposals for an automated loop that failed its quality assurance audit?▼

Yes, auditing loop execution generates ranked, actionable improvement proposals that refine loop structure and evaluator logic, providing scorecard reporting for automated software development workflows.

What specific failure signals should I look for when debugging state transitions in an FSM loop?▼

When debugging FSM loop state transitions, look for specific failure signals like evaluate errors, throttle stops, and over-escaped shell corruption by analyzing event logs and artifact mutations.