Add performance engineering skill.#233
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| def cmd_check(args): | ||
| names = {c for c, _, _ in failing(args.log)} | ||
| if args.expect: | ||
| expected = {ln.strip() for ln in open(args.expect) if ln.strip()} |
|
Accuracy: 2360 cases unchanged. |
… conclude Deliverables now live in a fixed, gitignored .perfeng/ (single static dir, never committed) instead of docs/perfeng/NNNN-<slug>/; removed the committed shared index. perf-init records the squash base in .perfeng/BASE. Agents commit self-contained source units during the loop (Phase E), and a new Phase G hands the run off: perf-conclude.sh squashes to one commit (reset --soft <base>), then offers push + PR to develop labelled perf-eng; package <N> renames .perfeng -> .perfeng-pr-<N> and builds a standard-ZIP perfeng-pr-<N>.zip to attach. Post-conclude follow-ups re-package and overwrite the archive in the existing PR comment.
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Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
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This PR adds a performance engineering skill in
.claude/skills/nfft-perf-engthat provides coding agents like Claude Code with a systematic approach to optimizing performance and accuracy of a scoped code block.The primary value of the skill is the systematic process that guides a coding agent through the optimization loop. At the beginning, performance and accuracy figures from benchmarks and tests are captured to provide an anchor to compare to. Before any code changes are accepted, the updated performance and accuracy figures are compare to the baseline to detect any regressions. The coding agent will systematically document each step taken and provide an HTML summary that can be used for human review.