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meta-optimize

Analyze ARIS usage logs to propose optimizations for SKILL.md files, reviewer prompts, and workflow defaults.

Updated Mar 1, 2026
Search Tags:#data-driven#log-analysis#workflow-improvement#patch-proposal#aris#harness-optimization
One-click install
npx skills add https://github.com/hve4638/hve-cc-marketplace --skill meta-optimize-hve4638
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: meta-optimize
Source: https://github.com/hve4638/hve-cc-marketplace/tree/main/aris/skills/meta-optimize
Command: npx skills add https://github.com/hve4638/hve-cc-marketplace --skill meta-optimize-hve4638

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze ARIS usage logs to propose optimizations for SKILL.md files, reviewer prompts, and workflow defaults, enabling data-driven improvements to harness performance.

Core Features & Use Cases

  • Analyze usage patterns across ARIS meta-events to identify frequently invoked skills, common parameter overrides, and failure modes.
  • Propose concrete, minimal patch changes to SKILL.md files with justification drawn from logs.
  • Facilitate cross-model review and structured reporting to guide safe, reversible changes.
  • Present an actionable optimization roadmap including evidence tables and next steps.

Quick Start

Run /meta-optimize to begin analyzing ARIS logs and generate patch recommendations.

Frequently Asked Questions about meta-optimize

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

FAQPage Schema
How do I analyze ARIS usage logs to optimize workflow performance?▼

You can analyze ARIS usage logs to optimize workflow performance by examining meta-events to identify frequently invoked skills, parameter overrides, and failure modes. This generates data-driven patch proposals for SKILL.md files and workflow defaults.

What are data-driven patch proposals for harness optimization?▼

Data-driven patch proposals are concrete, minimal changes to SKILL.md files and reviewer prompts derived from ARIS usage logs. They include specific justifications drawn from log patterns to guide safe, reversible modifications to workflow defaults.

How do I generate an optimization roadmap from ARIS meta-events?▼

To generate an optimization roadmap from ARIS meta-events, you analyze usage patterns across logs to produce actionable next steps. The roadmap includes evidence tables and structured reports to guide cross-model review and safe workflow changes.

Does ARIS harness optimization support cross-model review?▼

ARIS harness optimization supports cross-model review by facilitating structured reporting based on analyzed usage logs. This review mechanism ensures that proposed patch changes to SKILL.md files and workflow defaults remain safe and reversible.

When should I use data-driven patch proposals for SKILL.md files?▼

You should use data-driven patch proposals for SKILL.md files when monitoring harness performance across ARIS workflows reveals recurring failure modes or inefficient parameter overrides. This ensures changes are justified by logs and remain reversible.

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