/meta-optimize

name: meta-optimize
description: "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."
argument-hint: "[target-skill-or-all]"
allowed-tools: Bash(*), Read, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply, mcp__antigravity__antigravity, mcp__antigravity__antigravity-reply, mcp__grok__grok, mcp__grok__grok-reply, mcp__claude-review__review, mcp__claude-review__review_reply, mcp__claude-review__review_start, mcp__claude-review__review_reply_start, mcp__claude-review__review_status

Meta-Optimize: Outer-Loop Harness Optimization for ARIS

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md): > resolve invocation → `.aris/reviewer.json` → Claude Code default (Codex MCP) > with `reviewer_route.py` through the canonical helper resolver, preserving this > slot's tier, fresh contexts, source/image inputs and deterministic gates. > Codex examples/pins below apply only to `backend=codex`; other routes use their > own first-call/reply contract. Save the route and actual returned identity; > unavailable selections are `REVIEW_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`](../meta-apply/SKILL.md). 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):

• **No `Write`/`Edit` tool.** This skill cannot edit a SKILL.md / shared-reference /

any corpus file with the frictionless mutators. Its only outputs are the REPORT and staged patch files, written under `.aris/meta/` (a scratch area, never the corpus).

• **No apply step.** There is no in-skill "apply the patch" path (see Step 6). The

producer ends by *staging* approved patches for `/meta-apply`; a human must then invoke `/meta-apply` to land them. That human action is the landing gate.

• **`Bash` writes to the corpus are filtered, not impossible — be honest about the

layers.** What IS fully closed: the *accidental / in-flow* self-acquittal — this skill has no `Write`/`Edit` and no apply step, so an honest run cannot slip into editing the corpus. Defense-in-depth: install the [`corpus_write_guard`](../../templates/claude-hooks/corpus_write_guard.json) PreToolUse hook (like `meta_logging.json`), which DENIES the common Bash shell-writes (`>`, `tee`, `sed -i`, `cp`/`mv`, `touch`, `open(...,'w')`) to corpus paths. **This is a blacklist, NOT a complete sandbox** — a *deliberately* obscured Bash write (`git apply`, `patch`, `$var`/absolute paths, language file APIs) is not all caught. **Full structural prevention requires either removing this skill's `Bash` or an FS sandbox** — over-built for a not-yet-load-bearing producer, so deferred to when the gate carries real auto-modification volume (a brick-3 trigger). The intended backstop against a deliberate write is **detection, not prevention** — a corpus change with no valid/current `provenance` stamp (content-hash mismatch) *would be* catchable in a pre-push integrity check — but that verifier is **NOT yet built** (`provenance.py` has `content_hash` but no integrity-check subcommand, and no pre-push hook runs one). So today the deliberate-write case is neither prevented nor actively detected; track the integrity verifier as a follow-up before this producer goes load-bearing. Its legitimate Bash writes go only to `.aris/meta/`.

See [`shared-references/acceptance-gate.md`](../shared-references/acceptance-gate.md): a loop can DRIVE (propose, review) same-model, but the ACQUITTAL that lands a change must be cross-model (Step 4 jury) **and** the landing must be a separate human-gated act (`/meta-apply`).

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 | | MCP bridge config | Which reviewer model, routing rules | No (infra) |

**Not optimized**: The research artifacts themselves (papers, code, experiments). That's what the regular workflows do.

Prerequisites

1. **Logging must be active.** Copy `templates/claude-hooks/meta_logging.json` into your project's `.claude/settings.json` (or merge the hooks section). 2. **Sufficient data.** At least 5 complete workflow runs logged in `.aris/meta/events.jsonl`. The skill will check and warn if insufficient