FootballPrediction

Comprehensive API testing including unit tests, integration tests, performance testing, and automated test pipelines. Use when testing REST endpoints, validating API responses, load testing, or setting up test automation.

Manage code quality for Football Prediction project using Make commands. Use when running quality checks, formatting code, running tests, or preparing for commits. Supports black, flake8, mypy, bandit, pytest, and coverage tools.

Collect football data from external APIs including FotMob L2 data, odds information, and real-time match statistics. Use when gathering match data, collecting historical statistics, or updating prediction datasets.

Design and optimize data pipelines for football prediction system. Use when building ETL processes, data transformations, or optimizing data flow between PostgreSQL, Redis, and external APIs.

Manage PostgreSQL database operations including migrations, query optimization, connection pooling, and data backup/restore. Use when handling database tasks, optimizing queries, or managing database connections.

Manage application deployment including Docker containers, production environments, and service orchestration. Use when deploying to production, managing Docker services, or handling deployment rollbacks.

专业级容器化部署和自动化运维技能，基于FootballPrediction项目实战经验，提供Docker容器管理、权限修复、健康监控、故障诊断、一键部署等10个核心能力。

Containerize and deploy applications using Docker and DevOps best practices. Use when creating Dockerfiles, managing docker-compose, setting up CI/CD pipelines, or optimizing container orchestration.

Build high-performance async APIs using FastAPI for football prediction system. Use when creating REST endpoints, optimizing API performance, implementing authentication, or handling async database operations.

Professional feature engineering for football prediction. Use when extracting features, selecting important features, transforming data, or implementing V25.1 adaptive extraction engine (48→12061 dimensions).

Professional football match prediction and analysis using XGBoost 2.0+ ML model. Use when predicting match results, analyzing team performance, or calculating win probabilities. Features 67.2% accuracy with <100ms response time.

Optimize and maintain XGBoost machine learning models for football predictions. Use when tuning hyperparameters, implementing feature engineering, analyzing model performance, or deploying ML models to production.

Monitor system performance, prediction accuracy, and real-time metrics using Prometheus and Grafana. Use when checking system health, analyzing performance data, or tracking prediction accuracy.

Generate professional football analysis reports in PDF, Word, and Excel formats. Use when creating match analysis reports, data visualizations, or exporting prediction results. Includes professional charts and statistical analysis.

V26.1 production-grade data harvesting pipeline with zero defects. Use when running data collection, monitoring pipeline health, or managing batch processing for football prediction system.