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.

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

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.

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