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goupup-ai
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meta-optimize

Analyze ARIS usage logs to propose evidence-backed SKILL.md patch improvements.

2|Updated Aug 12, 2025
Search Tags:#skill optimization#workflow improvement#aris#usage log analysis#harness tuning#log-driven optimization
One-click install
npx skills add https://github.com/goupup-ai/miccai25 --skill meta-optimize-goupup-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: meta-optimize
Source: https://github.com/goupup-ai/miccai25/tree/main/ARIS/skills/meta-optimize
Command: npx skills add https://github.com/goupup-ai/miccai25 --skill meta-optimize-goupup-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually identifying inefficiencies in ARIS's skill prompts, default parameters, and workflow ordering is time-consuming and often relies on guesswork rather than actual user behavior data, leading to suboptimal harness performance and unnecessary manual user overrides.

Core Features & Use Cases

  • Usage Pattern Analysis: Parses ARIS event logs to identify frequently invoked skills, common parameter overrides, recurring tool failures, and unplanned user intervention points during workflows.
  • Evidence-Backed Optimization Proposals: Ranks improvement opportunities by expected impact and generates minimal, targeted patch proposals for SKILL.md files and workflow defaults, with clear data-backed rationales.
  • Cross-Model Advisory Review: Sends proposed patches to a separate reviewer model for adversarial scoring to ensure changes are safe, minimal, and well-supported before recommendation.
  • Use Case: If your team notices users repeatedly manually adjusting the default score threshold for the auto-review-loop skill, run meta-optimize to confirm the override frequency via log data and propose a calibrated default that reduces repetitive manual work.

Quick Start

Use the meta-optimize skill to analyze your accumulated ARIS usage logs and receive a prioritized report of evidence-backed improvements for your skill harness configuration.

Frequently Asked Questions about meta-optimize

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

FAQPage Schema
How do I optimize ARIS skill prompts using usage log analysis?▼

You can optimize ARIS skill prompts by parsing accumulated usage logs to identify frequent parameter overrides and tool failures, which generates evidence-backed, minimal patch proposals for SKILL.md files to improve harness performance.

What is the best way to reduce manual user overrides in ARIS workflows?▼

The best way to reduce manual user overrides in ARIS workflows is to analyze usage logs for frequent parameter adjustments and propose calibrated defaults that minimize repetitive manual work.

How does cross-model advisory review work for ARIS skill patches?▼

Cross-model advisory review for ARIS skill patches sends proposed workflow changes to a separate reviewer model for adversarial scoring, ensuring the adjustments are safe, minimal, and well-supported before recommendation.

Can I automatically apply SKILL.md patches generated from harness log analysis?▼

You cannot automatically apply SKILL.md patches generated from harness log analysis, as the system stages approved patches for separate human-gated application via the meta-apply skill.

Does meta-optimize require any specific dependencies to run?▼

meta-optimize requires no external dependencies, relying solely on accumulated ARIS research harness usage logs to identify inefficiencies in skill prompts, default parameters, and workflow ordering.

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