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meta-optimize

Analyze ARIS usage logs to propose minimal harness optimizations.

2|1|Updated Apr 19, 2026
Search Tags:#skill optimization#log analysis#patch generation#prompt tuning#multi-agent research#aris#workflow maintenance
One-click install
npx skills add https://github.com/raja21068/AutoResearch --skill meta-optimize-raja21068
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: meta-optimize
Source: https://github.com/raja21068/AutoResearch/tree/main/skills/aris/meta-optimize
Command: npx skills add https://github.com/raja21068/AutoResearch --skill meta-optimize-raja21068

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Meta-Optimize identifies recurring friction and failure patterns in ARIS usage logs so the harness itself can be improved, reducing manual overrides and stabilizing workflows over time.

Core Features & Use Cases

  • Log-driven harness diagnostics: Summarizes skill usage frequency, tool failures, parameter override trends, and review-loop convergence behavior from .aris/meta/events.jsonl.
  • Targeted optimization planning: Ranks the highest-impact harness components to adjust, such as reviewer prompts, default parameters, stopping/convergence rules, and workflow ordering.
  • Minimal safe patch proposals with review-gating: Generates constrained diffs, then cross-model reviews patches for evidence support and risk before recommending changes.
  • User-approved application workflow: Backs up SKILL.md, applies patches only after explicit approval, and records changes in .aris/meta/optimizations.jsonl.

Quick Start

Run meta-optimize to analyze your recorded ARIS event logs and receive a ranked set of safe, minimal SKILL.md harness 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 optimize workflow defaults from ARIS execution logs?▼

To optimize workflow defaults from ARIS execution logs, you analyze accumulated usage and failure patterns in `.aris/meta/events.jsonl` to propose minimal diffs for reviewer prompts, retry rules, and harness parameters.

What is the best way to improve reviewer prompts and convergence rules in ARIS?▼

Improving reviewer prompts and convergence rules in ARIS involves analyzing recorded event logs to identify friction patterns, ranking high-impact components, and generating constrained diffs for cross-model adversarial review.

How much logged data do I need before tuning ARIS harness parameters?▼

Tuning ARIS harness parameters requires a minimum amount of logged data within `.aris/meta/events.jsonl` to ensure sufficient usage and failure patterns exist for generating safe, minimal patch proposals.

Can I apply SKILL.md harness patches without manual approval?▼

No, you cannot apply SKILL.md harness patches without manual approval. The system backs up `SKILL.md`, performs cross-model adversarial review, and requires explicit user approval before applying changes and recording them in `.aris/meta/optimizations.jsonl`.

Does meta-optimize support targeted prompt tuning for a specific skill?▼

Yes, meta-optimize supports targeted prompt tuning by optionally scoping its analysis and proposed harness optimizations to a specific target skill, adjusting reviewer prompts and workflow defaults accordingly.

Why are my ARIS workflow overrides failing to converge during review loops?▼

ARIS workflow overrides failing to converge during review loops often stem from suboptimal default parameters or stopping rules. Analyzing execution-grounded logs helps identify these recurring friction patterns for patch generation.

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