auto-optimize

Orchestrate auditable double-layer delivery workflows for AI-generated artifacts.

Updated Mar 24, 2026
One-click install
npx skills add https://github.com/Airmomo/skills --skill auto-optimize-airmomo
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: auto-optimize
Source: https://github.com/Airmomo/skills/tree/main/auto-optimize
Command: npx skills add https://github.com/Airmomo/skills --skill auto-optimize-airmomo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Auto-optimize Skill enforces a rigorous, auditable workflow for AI-generated deliverables by combining a double-layer process: an outer multi-prompt phase and inner on-disk iterations with strict gates and verification.

Core Features & Use Cases

  • Outer phase: run multiple prompt schemes to align on scope and language.
  • Inner loop: iterate artifacts per round with on-disk diffs and per-round headers.
  • Verification: optional script and phase-04 with final delivery checks; host-agnostic integration.

Quick Start

Follow the recommended phases in order: read program.md and docs/verifiability.md, then implement phase 1 to phase 4 using the provided tracks and scripts.

Frequently Asked Questions about auto-optimize

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

FAQPage Schema
How do I make AI-generated deliverables auditable with strict phase gates?

You can make AI-generated deliverables auditable by enforcing a double-layer workflow with on-disk inner iterations, multi-scheme prompts, and strict phase gates. This ensures every artifact aligns with final delivery tracks through verification scripts.

What is a double-layer delivery workflow for AI prompts?

A double-layer delivery workflow combines an outer phase running multiple prompt schemes to align scope with an inner loop iterating artifacts per round. It includes on-disk diffs and per-round headers to ensure verifiable, auditable AI outputs.

Can I use this AI verification workflow across different IDEs and CLIs?

Yes, the AI verification workflow operates host-agnostic across IDEs, CLIs, or Skill-capable tools. It guides execution by reading core phase docs and SKILL.md while optionally loading scripts, references, and assets as needed.

How do I start implementing a multi-phase AI artifact delivery process?

To start a multi-phase AI artifact delivery process, read program.md and docs/verifiability.md first, then implement phase 1 through phase 4 sequentially using provided tracks and verification scripts to ensure final deliverables match expected tracks.

Why does my AI artifact generation lack verifiable on-disk diffs?

AI artifact generation lacks verifiable on-disk diffs when missing strict inner loop iterations with per-round headers. Enforcing a double-layer delivery workflow with phase gates and verification scripts ensures all artifacts are auditable and track-aligned.