From klingai-pack
Logs Kling AI video generation events to JSONL files and aggregates metrics for daily reports, cost analysis, and usage dashboards.
How this skill is triggered — by the user, by Claude, or both
Slash command
/klingai-pack:klingai-usage-analyticsThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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.
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_len": len(prompt),
})
def log_completion(self, task_id, status, elapsed_sec, credits_used):
self._write({
"event": "task_completed",
"task_id": task_id,
"status": status,
"elapsed_sec": elapsed_sec,
"credits_used": credits_used,
})
def log_error(self, task_id, error_type, message):
self._write({
"event": "task_error",
"task_id": task_id,
"error_type": error_type,
"message": message[:200],
})
from collections import defaultdict
class UsageAnalytics:
"""Aggregate metrics from JSONL event logs."""
def __init__(self, log_dir: str = "logs"):
self.log_dir = Path(log_dir)
def _read_events(self, date: str = None):
pattern = f"kling-{date}.jsonl" if date else "kling-*.jsonl"
events = []
for filepath in sorted(self.log_dir.glob(pattern)):
with open(filepath) as f:
for line in f:
events.append(json.loads(line))
return events
def daily_summary(self, date: str = None) -> dict:
date = date or datetime.utcnow().strftime("%Y-%m-%d")
events = self._read_events(date)
submitted = [e for e in events if e["event"] == "task_submitted"]
completed = [e for e in events if e["event"] == "task_completed"]
errors = [e for e in events if e["event"] == "task_error"]
succeeded = [e for e in completed if e["status"] == "succeed"]
failed = [e for e in completed if e["status"] == "failed"]
total_credits = sum(e.get("credits_used", 0) for e in completed)
avg_elapsed = (sum(e["elapsed_sec"] for e in succeeded) / len(succeeded)
if succeeded else 0)
by_model = defaultdict(int)
for e in submitted:
by_model[e["model"]] += 1
return {
"date": date,
"total_submitted": len(submitted),
"succeeded": len(succeeded),
"failed": len(failed),
"errors": len(errors),
"success_rate": f"{len(succeeded) / max(len(completed), 1) * 100:.1f}%",
"total_credits": total_credits,
"avg_generation_sec": round(avg_elapsed),
"by_model": dict(by_model),
}
def print_report(self, date: str = None):
s = self.daily_summary(date)
print(f"\n=== Kling AI Usage Report: {s['date']} ===")
print(f"Submitted: {s['total_submitted']}")
print(f"Succeeded: {s['succeeded']}")
print(f"Failed: {s['failed']}")
print(f"Success rate: {s['success_rate']}")
print(f"Credits used: {s['total_credits']}")
print(f"Avg time: {s['avg_generation_sec']}s")
print(f"By model:")
for model, count in s["by_model"].items():
print(f" {model}: {count}")
def cost_analysis(analytics: UsageAnalytics, days: int = 7):
"""Analyze cost trends over recent days."""
from datetime import timedelta
daily_costs = []
for i in range(days):
date = (datetime.utcnow() - timedelta(days=i)).strftime("%Y-%m-%d")
summary = analytics.daily_summary(date)
daily_costs.append({
"date": date,
"credits": summary["total_credits"],
"videos": summary["total_submitted"],
"estimated_usd": summary["total_credits"] * 0.14,
})
total_credits = sum(d["credits"] for d in daily_costs)
total_videos = sum(d["videos"] for d in daily_costs)
total_cost = sum(d["estimated_usd"] for d in daily_costs)
print(f"\n=== {days}-Day Cost Summary ===")
print(f"Total credits: {total_credits}")
print(f"Total videos: {total_videos}")
print(f"Est. cost: ${total_cost:.2f}")
print(f"Avg/day: ${total_cost / days:.2f}")
for d in daily_costs:
print(f" {d['date']}: {d['credits']} credits, {d['videos']} videos, ${d['estimated_usd']:.2f}")
import csv
def export_usage_csv(analytics: UsageAnalytics, output: str = "kling_usage.csv"):
events = analytics._read_events()
with open(output, "w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=["timestamp", "event", "task_id",
"model", "status", "credits_used",
"elapsed_sec"])
writer.writeheader()
for e in events:
writer.writerow({k: e.get(k, "") for k in writer.fieldnames})
print(f"Exported {len(events)} events to {output}")
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin klingai-packEnforces daily credit budgets for Kling AI video/image generation with per-request estimation by duration/mode/audio, threshold alerts, and usage tracking in Python.
Generates daily cost reports for Claude Code usage including total spend, running sessions, token stats via claude-view MCP tools. Useful for cost, spending, budget queries.
Generates an interactive HTML dashboard with cost breakdown, token usage, and 5-hour window timeline across all Claude Code sessions.