create-loop

Generate finite-state machine loop configurations with YAML and ll-loop CLI integration.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the fragility of AI coding sessions by enabling the creation of durable, state-checkpointed automation loops that survive terminal closures and context exhaustion.

Core Features & Use Cases

  • FSM Configuration: Guides you through building finite-state machine loops for tasks like fix-until-clean, metric-driven optimization, and multi-agent pipelines.
  • Validation & Testing: Automatically validates loop configurations and provides a test-run harness to verify logic before deployment.
  • Use Case: If you need to maintain a codebase's linting and type-checking standards, use this skill to create a persistent guardian loop that automatically runs checks and applies fixes until the constraints are satisfied.

Quick Start

Use the create-loop skill to generate a new automation loop that runs mypy and ruff until both tools pass.

Frequently Asked Questions about create-loop

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

FAQPage Schema
How do I create durable automation loops that survive terminal closures?

Durable automation loops are created by generating structured YAML configurations that checkpoint finite-state machine state to disk, allowing long-horizon AI software development tasks to resume execution after terminal closures or context exhaustion.

How do I configure finite-state machine loops for long-horizon AI coding tasks?

Finite-state machine loops are configured by defining state transitions and validation logic within structured YAML files, which are then integrated with the ll-loop CLI for persistent execution and automated state checkpointing.

Can I validate loop logic before deploying an automation workflow?

Loop logic can be validated before deployment using an automatic validation feature and a test-run harness, which verify the finite-state machine configuration and transition rules to ensure the automation workflow operates correctly.

What is the best way to maintain linting and type-checking standards automatically?

To maintain linting and type-checking standards, create a persistent guardian loop using this skill that automatically runs checks like mypy and ruff, applying fixes until all constraints are satisfied.

Do I need the ll-loop CLI to run state-checkpointed automation loops?

The ll-loop CLI is required for persistent execution, as the skill generates structured YAML configurations that integrate directly with the CLI to facilitate state checkpointing and durable workflow execution.

Why does my AI coding session lose progress during long-horizon tasks?

AI coding sessions lose progress because they lack state checkpointing; this skill solves that fragility by generating finite-state machine loops that persist execution state to disk, surviving context exhaustion and terminal closures.