meta-optimize
Meta-Optimize: Outer-Loop Harness Optimization for ARIS
Analyze accumulated usage logs and propose optimizations for: $ARGUMENTS
Context
ARIS is a research harness — a system of skills, bridges, workflows, and artifact contracts that wraps around LLMs to orchestrate research. This skill implements a prototype outer loop that observes how the harness is used and proposes improvements to the harness itself (not to the research artifacts it produces).
Inspired by Meta-Harness (Lee et al., 2026): the key insight is that harness design matters as much as model weights, and harness engineering can be partially automated by logging execution traces and using them to guide improvements.
What This Skill Optimizes (Harness Components)
| Component | Example | Optimizable? |
|---|---|---|
| SKILL.md prompts | Reviewer instructions, quality gates, step descriptions | Yes |
| Default parameters | difficulty: medium, MAX_ROUNDS: 4, threshold: 6/10 |
Yes |
| Convergence rules | When to stop the review loop, retry counts | Yes |
| Workflow ordering | Skill chain sequence within a workflow | Yes |
| Artifact schemas | What fields go in EXPERIMENT_LOG.md, idea-stage/IDEA_REPORT.md | Cautious |
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