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rs-plan

Plan verifiable improvements to an assessed AI skillset from quality assessments.

1|Updated Feb 12, 2026
Search Tags:#planning#quality assurance#ai development#iterative development#improvement#skillset
One-click install
npx skills add https://github.com/monkeypants/consultamatron --skill rs-plan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rs-plan
Source: https://github.com/monkeypants/consultamatron/tree/main/commons/skillset_engineering/skills/rs-plan
Command: npx skills add https://github.com/monkeypants/consultamatron --skill rs-plan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines the process of planning and implementing improvements to existing AI skillsets, ensuring targeted, verifiable enhancements.

Core Features & Use Cases

  • Quality Assessment Integration: Reads agreed-upon quality assessments to identify critical issues.
  • Scope Definition: Selects high-impact issues for improvement based on defined criteria (impact, independence, verifiability, proportionality).
  • Change Design: Details specific modifications to skill files, bytecode, pipelines, presenters, or infrastructure.
  • Verification Planning: Outlines automated and manual checks, including regression tests.
  • Use Case: After an AI skillset has been assessed for quality, this Skill will generate a detailed plan to fix the identified bugs and enhance its functionality, ensuring the changes are measurable and agreed upon before implementation.

Quick Start

Use the rs-plan skill to create an improvement plan for the current skillset based on the assessment.agreed.md file.

Frequently Asked Questions about rs-plan

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

FAQPage Schema
How do I plan verifiable improvements to an AI skillset after a quality assessment?▼

The improvement planning mechanism works by reading an `assessment.agreed.md` file to identify critical quality issues. It then selects high-impact problems based on independence and proportionality to design specific changes for skill files, bytecode, pipelines, or infrastructure.

What do I need to generate an AI skillset improvement plan?▼

Yes, the generated improvement plan outlines both automated and manual verification checks, including regression tests. This ensures that modifications to skill files, bytecode, pipelines, presenters, or infrastructure are measurable and verifiable before implementation.

How do I select which AI skillset bugs to fix during iterative development?▼

This targeted improvement planning approach distinguishes itself by requiring an agreed quality assessment to define strict acceptance criteria before implementation. It focuses on measurable outcomes and verifiable changes rather than generalized skillset modifications.

Can I plan modifications to AI skill infrastructure without an agreed quality assessment?▼

No, you cannot plan modifications without a prior quality assessment (`assessment.agreed.md`). The planning process strictly requires this file to define the scope, identify high-impact issues, and establish verifiable acceptance criteria for any infrastructure changes.

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