klingai-usage-analytics

name	klingai-usage-analytics
description	Build usage analytics and reporting for Kling AI video generation. Use when tracking patterns, analyzing costs, or building dashboards. Trigger with phrases like 'klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats'.
allowed-tools	Read, Write, Edit, Bash(npm:*), Grep
version	1.18.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","kling-ai","analytics","reporting"]
compatibility	Designed for Claude Code

Kling AI Usage Analytics

Overview

Track video generation usage with structured logging, aggregate metrics, daily reports, and cost analysis. Built on JSONL event logs that can feed into any analytics platform.

Event Logger

import json
import time
from datetime import datetime
from pathlib import Path

class KlingEventLogger:
    """Append-only JSONL event log for Kling AI operations."""

    def __init__(self, log_dir: str = "logs"):
        self.log_dir = Path(log_dir)
        self.log_dir.mkdir(exist_ok=True)

    def _write(self, event: dict):
        date = datetime.utcnow().strftime("%Y-%m-%d")
        filepath = self.log_dir / f"kling-{date}.jsonl"
        event["timestamp"] = datetime.utcnow().isoformat()
        with open(filepath, "a") as f:
            f.write(json.dumps(event) + "\n")

    def log_submission(self, task_id, prompt, model, duration, mode):
        self._write({
            "event": "task_submitted",
            "task_id": task_id,
            "model": model,
            "duration": int(duration),
            "mode": mode,
            : (prompt),
        })

     ():
        ._write({
            : ,
            : task_id,
            : status,
            : elapsed_sec,
            : credits_used,
        })

     ():
        ._write({
            : ,
            : task_id,
            : error_type,
            : message[:],
        })