infa-optimize

# `infa-optimize` — Spark performance suggestions Runs `infa2aidp cli optimize` over already-generated `.ipynb`/`.py` files and reports Spark performance anti-patterns, with a subset auto-fixable. ## When to use - After [`infa-migrate-mapping`](../infa-migrate-mapping/SKILL.md) (unless `--skip-optimize` was passed, in which case this already ran as part of that command). - The user asks "any performance issues", "optimize these notebooks". ## Canonical invocation ```bash PYTHONPATH=engine python3 -m infa2aidp.cli optimize \ -i <migrate-output-dir> \ -o ./optimization_report ``` Add `--auto-apply` to mechanically rewrite the subset of suggestions the optimizer considers safe to apply without a human decision — the notebook files under `<input>/` are modified in place when this flag is set. ## Flags | Flag | Default | Notes | |---|---|---| | `-i, --input` | required | Must be a directory; recurses for `.ipynb` and `.py` files | | `-o, --output` | `./optimization_report` | | | `--auto-apply` | off | Rewrites files in place for auto-applicable suggestions only | ## Output Console: `<N> notebook(s): <T> suggestion(s), <A> auto-applicable -> <output dir>`. Writes `optimization_report.md`. ## What this is not - Not a correctness check. A notebook can be Spark-performant and still be wrong — use [`infa-reconcile`](../infa-reconcile/SKILL.md) for correctness against real data. - Not a live-cluster profiling tool. All analysis is static, over the generated source — nothing here runs on AIDP. ## After this If `--auto-apply` was used, re-run [`infa-review`](../infa-review/SKILL.md) or at least diff the modified notebooks before [`infa-deploy`](../infa-deploy/SKILL.md) — an automatic rewrite is still a code change worth a look.