detecting-performance-regressions
This skill detects performance regressions by comparing current metrics against historical baselines in CI/CD pipelines. Use it to identify performance degradation, analyze benchmark results, and prioritize which metrics need investigation, then follow up with optimization advice or systematic debugging as needed.
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/detecting-performance-regressions && cp -r /tmp/detecting-performance-regressions/bundled/skills/detecting-performance-regressions ~/.claude/skills/detecting-performance-regressionsSKILL.md
# Performance Regression Detector ## Positioning Treat this skill as an explicit/manual helper for benchmark comparison work. ## When to Use Use this skill when: - Identify performance regressions in a CI/CD pipeline. - Analyze performance metrics for potential degradation. - Compare current performance against historical baselines. ## Not For / Boundaries - Broad optimization roadmap or tuning backlog: use `providing-performance-optimization-advice` - Root-cause debugging of a specific slow path: use `systematic-debugging` - General test artifact packaging without regression judgment: use `generating-test-reports` ## Typical Outputs - A baseline-vs-current regression summary - Severity-ranked regressed metrics - Follow-up questions for deeper profiling or investigation ## Related Skills - `performance-testing` for benchmark generation - `providing-performance-optimization-advice` after the regression is confirmed
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Full-stack software development agent for design, implementation, testing, and deployment. Use when the user explicitly asks for end-to-end project creation, feature development, bug fixing, or code refactoring.
Unified biological database evidence owner. Use for gene annotation, variant clinical significance, cancer mutation evidence, GWAS trait associations, pathway mapping, target-disease evidence, protein structures, protein interaction networks, reference single-cell census queries, and cross-database biological ID mapping. Do not use for full single-cell analysis, bulk RNA-seq differential expression, BAM/VCF processing, protein embedding models, metabolic flux modeling, genomic interval ML, or flow-cytometry file parsing.