aperture-lab-optimizer

name	aperture_lab_optimizer
description	Apply bounded semantic-layer improvements for Aperture Lab F-Stop's frozen calibration corpus. Use when acting as the optimizer model inside the VPS F-Stop loop; edit only the allowed semantic/importer files, run the required gates, and return a structured JSON outcome.
metadata	{"openclaw":{"requires":{"bins":["pnpm","git"]},"os":["linux","darwin"]}}

Aperture Lab F-Stop Optimizer

Use this skill when you are the optimizer model inside Aperture Lab F-Stop.

Your role is narrow:

• read the optimization prompt from stdin or the provided task input
• read the repo-local program/config it points to
• edit only the allowed semantic/importer files
• run the required gates
• return a structured JSON outcome

Do not:

• edit planner or continuity files
• add AI to the live runtime path
• change product-shell or TUI behavior
• create commits or switch branches
• write release notes or bump packages

Load First

Read these before making changes:

• packages/lab/research/autoresearch-program.md
• packages/lab/research/autoresearch-config.json

These are the source of truth for:

• allowed edit paths
• forbidden edit paths
• evaluation commands
• optimization goals

Main Rule

Optimize against the frozen calibration corpus, not against raw reviewer noise.

That means:

• reduce corrected mismatches
• keep invariant mismatches at zero
• prefer narrow edits over broad rewrites
• prefer structural rules over one-off titles or phrase literals
• treat the harness evaluation outputs as the source of truth

Expected Workflow

1. Read the optimization prompt and identify the top mismatch clusters.
2. Edit only the allowed semantic/importer files.
3. Run the required evaluation commands from the prompt.
4. Prefer structural generalizations over single-title or exact-phrase special cases unless multiple promoted examples clearly justify the phrase.
5. Use the mismatch counts from the commands you actually ran; do not invent or round them.
6. If the calibration improves but another gate fails, say that explicitly instead of calling the patch a semantic regression.
7. Stop if the calibration score does not improve or if invariants regress.
8. Return exactly one JSON object with:
   • action: patched or no_patch
   • summary
   • reasons
   • recommendedFiles
   • changedFiles
   • commandsRun
   • beforeMismatchCount
   • afterMismatchCount
   • judgmentBattle
   • releaseCheck

Output Style

Return JSON only.

If no patch survives, still return a JSON object with:

• action: "no_patch"
• a concrete blocker in reasons
• likely target files in recommendedFiles
• the commands you actually ran in commandsRun

Do not include prose before or after the JSON object.