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)

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.

Workflow

Step 0: Check Data Availability

EVENTS_FILE=".aris/meta/events.jsonl"
if [ ! -f "$EVENTS_FILE" ]; then
    echo "ERROR: No event log found at $EVENTS_FILE"
    echo "Enable logging first: copy templates/claude-hooks/meta_logging.json into .claude/settings.json"
    exit 1
fi

EVENT_COUNT=$(wc -l < "$EVENTS_FILE")
SKILL_INVOCATIONS=$(grep -c '"skill_invoke"' "$EVENTS_FILE" || echo 0)
SESSIONS=$(grep -c '"session_start"' "$EVENTS_FILE" || echo 0)

echo "📊 Event log: $EVENT_COUNT events, $SKILL_INVOCATIONS skill invocations, $SESSIONS sessions"

if [ "$SKILL_INVOCATIONS" -lt 5 ]; then
    echo "⚠️  Insufficient data (<5 skill invocations). Continue using ARIS normally and re-run later."
    exit 0
fi

Step 1: Analyze Usage Patterns

Read .aris/meta/events.jsonl and compute:

Frequency analysis:

• Which skills are invoked most often?
• Which slash commands do users type most?
• What parameter overrides are most common? (These suggest bad defaults.)

Failure analysis:

• Which tools fail most often? In which skills?
• What error patterns repeat? (OOM, import, compilation, timeout)
• How many auto-debug retries per workflow run?

Convergence analysis (for auto-review-loop):

• Average rounds to reach threshold
• Score trajectory shape (fast improvement? plateau? oscillation?)
• Which review round catches the most critical issues?
• Do users override difficulty mid-run?

Human intervention analysis:

• Where do users interrupt with manual prompts during workflows?
• What manual corrections do users make most? (These indicate skill gaps.)

Present findings as a structured summary table.

Step 2: Identify Optimization Targets

Based on Step 1, rank optimization opportunities by expected impact:

## Optimization Opportunities (ranked)

| # | Target | Signal | Proposed Change | Expected Impact |
|---|--------|--------|-----------------|-----------------|
| 1 | auto-review-loop default threshold | Users override to 7/10 in 60% of runs | Change default from 6/10 to 7/10 | Fewer manual overrides |
| 2 | experiment-bridge retry count | 40% of runs hit max retries on OOM | Add OOM-specific recovery (reduce batch size) | Fewer failed experiments |
| 3 | paper-write de-AI patterns | Users manually fix "delve" in 80% of runs | Add "delve" to default watchword list | Fewer manual edits |

If $ARGUMENTS specifies a target skill, focus analysis on that skill only. If $ARGUMENTS is empty or "all", analyze all skills with sufficient data.

Step 3: Generate Patch Proposals

For each optimization target, generate a concrete diff:

--- a/skills/auto-review-loop/SKILL.md
+++ b/skills/auto-review-loop/SKILL.md
@@ -15,7 +15,7 @@
 ## Constants
 
-- **SCORE_THRESHOLD = 6** — Minimum review score to accept.
+- **SCORE_THRESHOLD = 7** — Minimum review score to accept. (Raised based on usage data: 60% of users overrode to 7+.)

Rules for patch generation:

• One patch per optimization target
• Each patch must include a comment explaining WHY (with data from the log)
• Patches must be minimal — change only what the data supports
• Never change artifact schemas or MCP bridge config in v1
• Never change behavior that would break existing user workflows

Step 4: Cross-Model Review of Patches

Send each patch to GPT-5.4 xhigh for adversarial review:

mcp__codex__codex:
  model: gpt-5.4
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    You are reviewing a proposed optimization to an ARIS SKILL.md file.
    
    ## Original Skill (relevant section)
    [paste original]
    
    ## Proposed Patch
    [paste diff]
    
    ## Evidence from Usage Log
    [paste summary stats]
    
    Review this patch:
    1. Does the evidence support the change?
    2. Could this change hurt other use cases?
    3. Is the change minimal and safe?
    4. Score 1-10: should this be applied?
    
    If score < 7, explain what additional evidence would be needed.

Step 5: Present Results

Output a structured report:

# ARIS Meta-Optimization Report

**Date**: [today]
**Data**: [N] events, [M] skill invocations, [K] sessions
**Target**: [skill name or "all"]

## Proposed Changes

### Change 1: [title]
- **Target**: [skill/file:line]
- **Signal**: [what the data shows]
- **Patch**: [diff]
- **Reviewer Score**: [X/10]
- **Reviewer Notes**: [summary]
- **Status**: ✅ Recommended / ⚠️ Needs more data / ❌ Rejected

### Change 2: ...

## Changes NOT Made (insufficient evidence)
- [pattern observed but too few samples]

## Recommendations
- [ ] Apply Change 1 (reviewer approved)
- [ ] Collect more data for Change 3 (need N more runs)
- [ ] Consider manual review of Change 2

## Next Steps
Run `/meta-optimize apply 1` to apply a specific change, or
`/meta-optimize apply all` to apply all recommended changes.

Step 6: Apply Changes (if user approves)

If user runs /meta-optimize apply [N]:

1. Back up original SKILL.md to .aris/meta/backups/
2. Apply the patch
3. Log the change to .aris/meta/optimizations.jsonl
4. Remind user to test the changed skill on their next run

Never auto-apply without user approval.

Key Rules

• Log-driven, not speculative. Every proposed change must cite specific data from the event log. No "I think this would be better."
• Minimal patches. Change one thing at a time. Don't rewrite entire skills.
• Reviewer-gated. Every patch goes through cross-model review before recommendation.
• Reversible. Always back up before applying. Always log what changed.
• User-approved. Never auto-apply. Present, explain, let the user decide.
• Honest about uncertainty. If the data is insufficient, say so. Don't optimize on noise.
• Portable. Optimizations should improve the skill for all users, not just one user's style. If a change seems user-specific, flag it.

Event Schema Reference

The log at .aris/meta/events.jsonl contains JSONL records with these shapes:

{"ts":"...","session":"...","event":"skill_invoke","skill":"auto-review-loop","args":"difficulty: hard"}
{"ts":"...","session":"...","event":"PostToolUse","tool":"Bash","input_summary":"pdflatex main.tex"}
{"ts":"...","session":"...","event":"codex_call","tool":"mcp__codex__codex","input_summary":"review..."}
{"ts":"...","session":"...","event":"tool_failure","tool":"Bash","input_summary":"python train.py"}
{"ts":"...","session":"...","event":"slash_command","command":"/auto-review-loop","args":""}
{"ts":"...","session":"...","event":"user_prompt","prompt_preview":"change difficulty to hard"}
{"ts":"...","session":"...","event":"session_start","source":"startup","model":"claude-opus-4-6"}
{"ts":"...","session":"...","event":"session_end"}

Triggering

This skill is NOT part of the standard W1→W1.5→W2→W3→W4 pipeline. It is a maintenance workflow with three trigger mechanisms:

1. Passive logging (always on): Claude Code hooks record events to .aris/meta/events.jsonl automatically during normal usage. Zero user effort.

2. Automatic readiness check (SessionEnd hook): When a Claude Code session ends, check_ready.sh counts skill invocations since the last /meta-optimize run. If ≥5 new invocations have accumulated, it prints a reminder:

   Copy

   📊 ARIS has logged 8 skill runs since last optimization. Run /meta-optimize to check for improvement opportunities.
   

   This is a suggestion only — it does not auto-run optimization.

3. Manual trigger: User runs /meta-optimize when they see the reminder or whenever they want.

After each /meta-optimize run, the skill writes the current timestamp to .aris/meta/.last_optimize so the readiness check only counts new invocations.

Acknowledgements

Inspired by Meta-Harness (Lee et al., 2026) — end-to-end optimization of model harnesses via filesystem-based experience access and agentic code search.