iterate-until-good

Iteratively improve code and pull requests until all quality categories exceed 85%.

3|1|Updated Dec 3, 2025
One-click install
npx skills add https://github.com/nsheaps/ai-mktpl --skill iterate-until-good
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: iterate-until-good
Source: https://github.com/nsheaps/ai-mktpl/tree/main/plugins/scm-utils/skills/iterate-until-good
Command: npx skills add https://github.com/nsheaps/ai-mktpl --skill iterate-until-good

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured, repeatable review-and-improve workflow that evaluates code, pull requests, branches, or documents across multiple quality categories and drives iterative changes until each category exceeds an 85% threshold while treating scores below 70% as a hard block.

Core Features & Use Cases

  • Multi-axis evaluation: Produce per-category 0-100 scores for simplicity, flexibility, usability, documentation and discoverability, security, pattern conformity, best practices, and general QA.
  • Agentic iteration: Launch background review agents to run parallel evaluations, generate detailed reports, leave inline PR comments, and iterate on the target until quality thresholds are met.
  • Auditable reports: Persist structured reports and supporting references into a consistent repository path for traceability and follow-up remediation.
  • Use Case: Use this Skill on a feature branch or pull request to identify weak areas (e.g., documentation, security) and automatically drive iterative fixes until the branch meets merging quality standards.

Quick Start

Run the iterate-until-good skill on the target pull request or branch and iterate until every category is above 85%.

Frequently Asked Questions about iterate-until-good

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

FAQPage Schema
How do I automatically iterate code quality on a pull request until it passes?

An agentic workflow evaluates pull requests across categories like simplicity, security, and documentation, launching background review agents to drive iterative fixes until all category scores exceed an 85% threshold.

What categories are evaluated during automated code quality scoring?

Automated code quality scoring evaluates simplicity, flexibility, usability, documentation and discoverability, security, pattern conformity, best practices, and general QA, generating per-category 0-100 scores.

What is the minimum passing score for automated pull request reviews?

The minimum passing score for automated pull request reviews is 85% across all evaluated categories. Any category scoring below 70% is treated as a hard block, preventing the iteration from completing successfully.

Can I use background agents to run parallel code reviews on a feature branch?

Yes, you can launch background review agents to run parallel evaluations on a feature branch. These agents generate detailed reports, leave inline PR comments, and iterate on the target until quality thresholds are met.

How does iterative code review handle security and documentation gaps?

Iterative code review handles security and documentation gaps by scoring these axes from 0-100, generating detailed reports with supporting references, and driving automated changes until both security and documentation exceed the 85% threshold.

Are structured reports persisted when running automated branch quality assessments?

Yes, structured reports and supporting references are persisted into a consistent repository path during automated branch quality assessments, ensuring traceability and supporting follow-up remediation for identified weak areas.