game-analytics

Purpose

This skill analyzes game data metrics to track player behavior and optimize performance, processing logs, sessions, and metrics from games.

When to Use

Use this skill for processing in-game data during development, such as identifying player drop-off in levels, optimizing resource usage, or debugging performance bottlenecks in real-time multiplayer games.

Key Capabilities

• Parse JSON-formatted game logs to extract metrics like session duration and player actions (e.g., via claw game-analytics parse --input logs.json).
• Generate reports for behavior tracking, such as heatmaps of player movement using API endpoint GET /api/analytics/reports/heatmap?gameId=123.
• Perform optimization queries, like querying for high-latency events with SQL-like filters (e.g., config in YAML: metrics: [latency > 500ms]).
• Integrate with ML models for predictive analytics, e.g., predict churn based on play patterns using embedded functions like claw game-analytics predict --model churn.json.

Usage Patterns

Always initialize with authentication via environment variable $GAME_ANALYTICS_API_KEY. For CLI, run commands in a project directory with game data files. In code, import as a module and call functions directly. Use asynchronous patterns for large datasets to avoid blocking. For example, chain commands: first parse data, then analyze. Handle outputs as JSON streams for piping to other tools.