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.

Common Commands/API

• CLI Command: claw game-analytics analyze --file data.json --metric player-session --output report.csv (parses file, filters by metric, saves to CSV; requires $GAME_ANALYTICS_API_KEY).

• API Endpoint: POST /api/analytics/track with body { "event": "player_login", "data": { "sessionId": "abc123", "timestamp": "2023-10-01T12:00:00Z" } } (sends tracking data; authenticate with Bearer token from $GAME_ANALYTICS_API_KEY).

• Code Snippet (Python): import claw api_key = os.environ.get('GAME_ANALYTICS_API_KEY') response = claw.analytics.track(event='level_complete', data={'level': 5}, api_key=api_key) print(response.json())

• Config Format: YAML file for custom metrics, e.g., metrics:

  • name: session_length threshold: 300 # seconds filters:
    • player_type: 'new'

Load with claw game-analytics load-config config.yaml .

Integration Notes

Integrate by setting $GAME_ANALYTICS_API_KEY in your environment before running commands. For web apps, use OAuth via claw game-analytics auth --provider google to get a token. In Node.js projects, require the module and handle promises:

const claw = require('claw'); claw.analytics.setKey(process.env.GAME_ANALYTICS_API_KEY); claw.analytics.analyze({ file: 'data.json' }).then(data => console.log(data));

Ensure data formats match (e.g., JSON inputs only). For cluster integration, link with 'game-dev' tools by prefixing commands, like claw game-dev game-analytics analyze .

Error Handling

Check for errors by inspecting exit codes in CLI (e.g., code 401 means auth failure; retry with claw game-analytics retry --command analyze ). In API calls, catch HTTP errors: if status 403, log "Invalid API key" and prompt for $GAME_ANALYTICS_API_KEY reset. Use try-except in code snippets:

try: result = claw.analytics.analyze(file='data.json') except claw.AnalyticsError as e: if e.code == 'AUTH_FAILED': print("Set GAME_ANALYTICS_API_KEY and retry")

Always validate inputs before processing to avoid parsing errors (e.g., ensure JSON is well-formed).

Concrete Usage Examples

• To track player behavior in a mobile game: First, export logs to JSON, then run claw game-analytics analyze --file player_logs.json --metric drop-off to identify levels with high quit rates. Use the output to adjust game design, e.g., via API: POST /api/analytics/optimize with { "suggestions": true }.

• For performance optimization in a multiplayer server: Load config with claw game-analytics load-config perf.yaml , then execute claw game-analytics query --metric latency --filter 'server=prod' to get reports. Integrate into a CI/CD pipeline to automate checks, using code: claw.analytics.query({ metric: 'latency' }).then(results => { if (results.avg > 100) console.log('Optimize server'); });

Graph Relationships

• Related to: game-dev cluster (parent), player-tracking skill (dependency), data-metrics skill (sibling).

• Connects with: game-logging for input data, performance-optimization for action outputs.