Core

name: agent-analytics description: Tracks key performance indicators (KPIs) for AI Agents: Token Usage, Task Duration, Loop Cycles, and Success Rate. triggers: [analytics, metrics, tokens used, cost tracking, performance report, agent stats] context_cost: low

Agent Analytics Skill

Goal

Provide visibility into the "Black Box" of agent execution by tracking cost (tokens) and efficiency (time/loops).

Flow

1. Metric Capture

Input: Completion of a Task / Tool Call / Phase. Action: Log the following structured data:

• timestamp: ISO 8601
• agent_id: Loki / Claude / Gemini
• task_id: T-NNN
• tokens_in: (Estimated)
• tokens_out: (Estimated)
• duration_ms: Execution time
• status: SUCCESS | FAILURE | RETRY

2. Analysis & Alerts

• Loop Detection: If task_id appears > 5 times in metrics.log with status: RETRY, trigger human_escalation.
• Cost Anomaly: If tokens_out > 5000 for a simple task, flag as "Verbose/Inefficient".

3. Reporting

Command: generate-report Output: metrics/weekly_report.md

• Total Tokens consumed.
• Average Task Duration.
• Success Rate % (First-pass vs Retry).

Storage

• agents/memory/metrics/analytics.jsonl (Append-only log)