test-environment-management
Managing test environments including provisioning, configuration, data seeding, isolation, cleanup, and environment-as-code patterns.
Managing test environments including provisioning, configuration, data seeding, isolation, cleanup, and environment-as-code patterns.
Detecting and diagnosing flaky tests using statistical analysis, execution recording, environmental factor isolation, and quarantine strategies.
Validating A/B test implementations including traffic splitting accuracy, statistical significance calculation, metric tracking, and experiment cleanup.
Advanced mutation testing using Stryker, PIT, and mutmut to measure test suite quality by introducing controlled mutations and tracking kill rates.
Test fixture design patterns including builders, factories, object mothers, and fixture management for maintainable test data setup.
Testing distributed tracing implementations including context propagation, span correlation, baggage handling, and trace sampling verification.
Network latency simulation and testing for measuring application behavior under various network conditions including high latency, packet loss, and bandwidth constraints.
Stress testing methodologies for finding breaking points, resource exhaustion thresholds, and degradation patterns under extreme load conditions.
Guidance for deep knowledge graph traversal across memories, entities, and relationships. Use when needing comprehensive context before planning, investigating connections between concepts, or answering "what do you know about X" questions.
Coordinate plot point dramatic function analysis process, manage text preprocessing, parallel analysis, result integration. Suitable for plot point and dramatic function analysis of long texts, scenarios requiring structured analysis reports
Deep code scan for AI security issues — prompt injection, PII in prompts, hardcoded keys, unguarded agents.
Kubernetes alert triage — dedup via YT search, deep control plane investigation, auto-escalation for recurring/flapping/control-plane alerts.
Create and manage a SOC 2 risk register — auto-seeds from scan findings, tracks treatment, produces audit evidence.
Анализирует генетические данные пользователя из VCF файла. Используй когда пользователь спрашивает о своей генетике, наследственных признаках, предрасположенностях, метаболизме веществ (кофеин, алкоголь, лекарства), спортивных способностях, рисках заболеваний, питании на основе генов.
Triage a suspected malicious file hash. Use when investigating malware alerts or suspicious files. Analyzes GTI file report, behavioral indicators, identifies affected hosts, enriches network IOCs, and recommends containment actions.
Research competitors, analyze market positioning, identify differentiators, and understand competitive landscape to inform product strategy and positioning. Use when researching competitors, analyzing features, identifying market gaps, understanding pricing strategies, evaluating competitive advantages, researching market trends, or informing product decisions.
Research topics thoroughly and write comprehensive, well-structured content including articles, documentation, blog posts, and technical writing with proper citations and sources. Use when writing blog posts, creating technical articles, researching topics, gathering sources, writing documentation, creating educational content, or producing research-backed content.
Parse and create FCS (Flow Cytometry Standard) files v2.0-3.1. Read event data as NumPy arrays, extract channel metadata, handle multi-dataset files, export to CSV/FCS. For advanced gating and compensation use FlowKit.
Python image processing library for scientific microscopy and bioimage analysis. Read/write multi-format images, apply filters (Gaussian, median, LoG), segment objects (thresholding, watershed, active contours), measure region properties (area, intensity, shape), and detect features. Part of the SciPy ecosystem; integrates with NumPy arrays. Use OpenCV instead for real-time video processing; use CellPose for deep-learning cell segmentation; use napari for interactive visualization.
Python library for geospatial vector data analysis extending pandas with spatial operations. Covers reading/writing spatial formats (Shapefile, GeoJSON, GeoPackage, Parquet, PostGIS), coordinate reference systems, geometric operations (buffer, simplify, centroid, affine transforms), spatial analysis (joins, overlays, dissolve, clipping, distance), and visualization (choropleth, interactive maps, basemaps). Use when working with geographic data for spatial joins, overlay operations, coordinate transformations, area/distance calculations, or map creation.