Analyze QA timing data to identify performance bottlenecks and regressions.
npx skills add https://github.com/ttsmith21/swcsharpaddin --skill perf-ttsmith21
Please help me install this Agent Skill. Skill: perf Source: https://github.com/ttsmith21/swcsharpaddin/tree/main/.claude/skills/perf Command: npx skills add https://github.com/ttsmith21/swcsharpaddin --skill perf-ttsmith21
Analyze timing data from QA runs to identify performance bottlenecks and track regressions.
Run the skill after QA runs to identify slow operations and track regressions.
High-intent search queries and answers about installing and using this skill.
To find performance bottlenecks in QA timing data, this skill reads the latest timing.csv file, summarizes execution times by operation category, and highlights the slowest operations.
Tracking QA performance regressions is done by comparing current timing.csv data against an existing baseline, reporting operations that have significantly slowed down or improved over time.
Summarizing slow operations from a timing.csv file involves grouping execution durations by operation category to identify the most time-consuming tasks within your latest QA test run.
Yes, the performance analysis automatically targets the latest QA run by checking the tests/Run_Latest directory or falling back to the most recent timestamped QA run available.
Yes, you can detect both regressions and improvements in QA timing data by comparing current operation durations against a historical baseline and reporting the variances in a structured view.
The performance analysis outputs a structured performance view that summarizes timing by operation category, highlights slow operations, and details baseline comparisons for regressions and improvements.
terzigoluRun Warp integration tests with screenshot and video capture.
FrekiManagarmCompute truth scores and run correctness, security, performance, and documentation checks with JSON reports and Git rollback.
benjithompsonAnalyze BlazeMeter test performance data against a baseline to detect trends and regressions.
gitgoodordietryingProfile CPU and memory usage to diagnose performance bottlenecks.
