paseo-loop

Coordinate iterative worker-verifier loops with configurable stop conditions.

Updated Apr 25, 2026
One-click install
npx skills add https://github.com/KQDtianxiaK/AtHand --skill paseo-loop-kqdtianxiak
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: paseo-loop
Source: https://github.com/KQDtianxiaK/AtHand/tree/main/paseo-main/skills/paseo-loop
Command: npx skills add https://github.com/KQDtianxiaK/AtHand --skill paseo-loop-kqdtianxiak

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates iterative AI agent loops by coordinating a worker and a verifier in cycles until a defined exit condition, reducing manual supervision and speeding up complex tasks.

Core Features & Use Cases

  • Automated iteration cycles: Launch a worker, verify results, sleep between iterations, and repeat until stop criteria are met.
  • Flexible model selection: Independently choose different providers/models for the worker and the verifier.
  • Guardrails and archiving: Enforce max iterations and/or max runtime, with optional archiving of iteration history for debugging.

Quick Start

Start a loop by supplying a concrete worker prompt and verifier criteria, then run paseo loop with your desired stop conditions.

Frequently Asked Questions about paseo-loop

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

FAQPage Schema
How do I automate an iterative AI loop for bug-fixing without manual supervision?

You can automate iterative AI loops by coordinating a worker and a verifier in cycles until a defined exit condition is met, reducing manual supervision. This applies to repetitive bug-fixing iterations, data labeling, or progressive task completion.

Can I use different AI models for the worker and verifier in an automated loop?

Yes, you can independently choose different providers and models for the worker and the verifier in the automated loop. This flexible model selection allows you to optimize generation and verification separately.

What guardrails can I configure for AI agent iteration cycles?

You can enforce max iterations and max runtime as guardrails for AI agent iteration cycles. Optional archiving of iteration history is also supported for debugging and tracking progressive task completion.

How do I set stop conditions for an automated AI verification loop?

You set stop conditions for an automated AI verification loop by supplying concrete verifier criteria and defining exit parameters. The loop alternates between worker execution and verification until these stop criteria are met.

What is the best way to run progressive data labeling tasks with AI verification?

The best way to run progressive data labeling is to launch an iterative AI agent loop that alternates a worker and verifier with sleep intervals. This automates repetitive tasks with configurable guardrails and optional archiving.