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] tags: [core] context_cost: low

Agent Analytics Skill

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

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

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".

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

PII Scrubbing in Telemetry: Ensure that task_id or any logged payload data does not inadvertently capture and store user PII or raw authentication tokens in the analytics.jsonl file.
Log Forgery Prevention: The analytics logging mechanism must be isolated so that a compromised agent cannot forge or alter historical telemetry data to hide malicious activity or disguise token theft.

Cost Denial of Service (DoS): Tie the anomaly detection (e.g., tokens_out > 5000) directly to a hard circuit breaker that revokes the agent's API keys or suspends the session to prevent runaway financial billing attacks.
Secure Metric Access: The metrics/weekly_report.md and raw log files must be access-controlled, as traffic patterns and task duration metrics can leak business intelligence or identify high-value target processes to an attacker.

Analytics Manipulation Defense: Agents must not have write access to historical analytics.jsonl lines. They may only append new records.
Metric Hallucination Avoidance: If an LLM is used to summarize the weekly report, it must use hard math validation (e.g., via a Python script execution) rather than estimating or hallucinating aggregated token counts and success rates.