performance-regression

Runtime evidence that helps agents trace, profile, and burn down hotspots in application and native code, GPU kernels, and inference stacks.

Build trustworthy pytest-benchmark matrices, collect repeated runs, and detect performance regressions

Production-grade guides and runbooks for query plan baseline tracking, performance regression detection, and CI/CD gate automation — for database SREs and platform teams.

Example of automatically generated performance analysis reports — comparing builds, identifying regressions, and producing decision-ready conclusions from performance test data.

Profile a pytest suite, script, or callable and comment a CPU + memory hotspot diff on every pull request.

Evidence-backed performance regression guardrails for pull requests, with protected verification and bounded Codex repair workflows.

Claude Code skill to benchmark code, save baselines, compare stacks, and catch slowdowns. Built on affaan-m/ECC by @affaan-m.

Δ Single-host performance regression scope — A/B window diff & trends, one static Go binary

Continuous benchmarking and performance-regression tracking. Runs benchmarks, records baselines in a git branch, renders trend dashboards, and posts non-blocking baseline-vs-PR comparisons.

Compare Rust benchmark results across pull-request revisions