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Found 10 Skills
Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while golang-performance provides the optimization patterns.
Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.
Automated commit analysis and regression detection for AItrader. 自动化提交分析和回归检测。 Use this skill when: - Running regression checks after code changes (代码修改后运行回归检测) - Analyzing git commit history (分析 git 提交历史) - Using AI to analyze code changes (使用 AI 分析代码变更) - Checking if fix commits are still correctly applied (检查修复是否正确应用) - Before merging PRs (合并 PR 前) Keywords: commit, regression, analysis, git, fix, validation, AI, 提交, 回归, 分析
Use this skill to monitor a deployed URL for regressions after deploys, merges, or dependency upgrades.
Agent skill for benchmark-suite - invoke with $agent-benchmark-suite
Captures quality metrics baseline (tests, coverage, type errors, linting, dead code) by running quality gates and storing results in memory for regression detection. Use at feature start, before refactor work, or after major changes to establish baseline. Triggers on "capture baseline", "establish baseline", or PROACTIVELY at start of any feature/refactor work. Works with pytest output, pyright errors, ruff warnings, vulture results, and memory MCP server for baseline storage.
Integrates Lighthouse CI for automated performance testing, Core Web Vitals tracking, and regression detection in CI/CD pipelines. Use when user asks to "setup Lighthouse CI", "add performance testing", "monitor Core Web Vitals", or "prevent performance regressions".
Read every docs/benchmarks/runs/*.json and surface drift in win rate, latency, escalation rate, and LLM-baseline cost over time
Create and run LangWatch experiments for pre-deployment batch testing. Use when the user wants to test an agent against a dataset, compare prompts or models, benchmark quality, detect regressions, or add a CI quality gate. Do not use for production monitoring or guardrails.
Analyze Grafana Cloud k6 test run trends over time. Detects slow metric drift (e.g., P95 latency creeping up while still passing thresholds), computes headroom to thresholds, flags anomalies, and recommends threshold tightening. Use when the user asks about test performance trends, wants to know if metrics are degrading, asks whether thresholds should be tightened, or wants a health check across recent runs for a specific test. Trigger on phrases like "how is my test trending", "is P95 getting worse", "check for performance regression", "should I tighten thresholds", "are my tests degrading", "show me trends for test X", "analyze my k6 test runs", or "is my test getting slower". Also trigger when a user asks to check all tests in a project -- run this skill once per test and synthesize.