version: "1.0.0" name: inference-format-optimizer description: Iterative benchmarking, evaluation, and algorithmic optimization of alternative A2UI inference formats (such as Express, Atom, and Elemental). Trigger when asked to: (1) Run optimization passes or loops on an inference format, (2) Evaluate or benchmark format accuracy, latency, or token efficiency, (3) Create parallel worktree subagents for format iteration, or (4) Benchmark format trade-offs against baselines.

Inference Format Optimizer

This skill provides procedural workflows, CLI orchestrators, decision guardrails, and subagent protocols for iteratively optimizing A2UI inference formats.

Quick-Start CLI Cheatsheet

All execution scripts live under scripts/ in this skill:

Detailed References

• Scoring & Decision Rules: See references/scoring_model.md for $S_{\text{opt}}$ formula, correctness guardrails, and efficiency caps.
• Subagent Worktree Protocol: See references/subagent_protocol.md for launching subagents in isolated git worktrees.
• Subagent Prompt Template: See templates/subagent_prompt.md for launching pass tasks via invoke_subagent.

The 6-Step Optimization Workflow

1. Analyze History: Inspect past runs in eval/iterative_format_optimizer/history/<format>/ and read eval/iterative_format_optimizer/history_summary.md to avoid repeating past reverted hypotheses.
2. Implement Hypothesis: Modify compiler.py, prompt_generator.py, or parser.py under agent_sdks/python/a2ui_agent/src/a2ui/inference_formats/experimental/<format>/.
3. Run Unit Conformance Tests: Verify code changes pass pytest unit tests.
4. Execute Benchmark Evaluation: Run python scripts/optimize_format.py --format <format>.
5. Evaluate Decision Rules:

• Must pass Pytest and maintain baseline accuracy.
• Code Output Tokens must NOT expand $> +5\%$.
• Keep change if composite score $S_{\text{opt}}$ improves; revert otherwise (git reset --hard HEAD).

1. Archive & Synchronize: Archive run with --archive and update history index using python scripts/sync_history.py.