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

LLM agent that turns two Android Perfetto traces and a git range into a cited diagnosis: the regressed metric, the culprit commit, and the trace rows that prove it. Every claim is re-verified by code; measured on planted regressions.

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.

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

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

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

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

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

JMeter-based performance testing solution built under a critical production performance incident, demonstrating stateful workload modelling, observability, capacity testing, and iterative performance engineering.

Planted performance regressions in Google's JetNews sample (Jetpack Compose) with Perfetto captures: startup I/O, allocation storm, main-thread sleep, layout thrash, listener leak. Ground truth for evaluating Android performance tools.

Compare Rust benchmark results across pull-request revisions