Meta Optimize
Analyze ARIS usage logs and propose optimizations to SKILL.md files, reviewer prompts...
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-optimize -g -a claude-code -y- Repository
- wanshuiyin/Auto-claude-code-research-in-sleep18.4k
- Category
- Engineering
- Updated
- 4d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Analyze ARIS usage logs and propose optimizations to SKILL.md files, reviewer prompts, and workflow defaults. Outer-loop harness optimization inspired by Meta-Harness (Lee et al., 2026). Use when user says \"优化技能\", \"meta optimize\", \"improve skills\", \"分析使用记录\", or wants to optimize ARIS's own harness components based on accumulated experience.
Meta-Optimize: Outer-Loop Harness Optimization for ARIS
Before each reviewer slot, follow Selectable Reviewers: resolve invocation →
.aris/reviewer.json→ Claude Code default (Codex MCP) withreviewer_route.pythrough the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only tobackend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections areREVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.
Analyze accumulated usage logs and propose optimizations for: $ARGUMENTS
Privilege boundary, this skill is a READ-ONLY PRODUCER
meta-optimize proposes; it does not land. The mutation of the skill corpus
is the exclusive job of a separate, human-invoked skill: /meta-apply.
This split is structural, not advisory, it is why a missed instruction cannot let
this loop apply its own patch (the self-acquittal failure mode):
