jeremylongshore

klingai-usage-analytics

@jeremylongshore/klingai-usage-analytics
jeremylongshore
1,004
123 forks
Updated 1/18/2026
View on GitHub

Build usage analytics and reporting for Kling AI. Use when tracking generation patterns, analyzing costs, or creating dashboards. Trigger with phrases like 'klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats'.

Installation

$skills install @jeremylongshore/klingai-usage-analytics
Claude Code
Cursor
Copilot
Codex
Antigravity

Details

Pathplugins/saas-packs/klingai-pack/skills/klingai-usage-analytics/SKILL.md
Branchmain
Scoped Name@jeremylongshore/klingai-usage-analytics

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

skills list

Skill Instructions


name: klingai-usage-analytics description: | Build usage analytics and reporting for Kling AI. Use when tracking generation patterns, analyzing costs, or creating dashboards. Trigger with phrases like 'klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats'. allowed-tools: Read, Write, Edit, Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

Klingai Usage Analytics

Overview

This skill shows how to build comprehensive usage analytics including generation metrics, cost analysis, trend reporting, and visualization dashboards for Kling AI.

Prerequisites

  • Kling AI API key configured
  • Usage data collection in place
  • Python 3.8+ with pandas/matplotlib (optional)

Instructions

Follow these steps for analytics:

  1. Collect Data: Capture usage events
  2. Aggregate Metrics: Calculate key metrics
  3. Generate Reports: Create usage reports
  4. Visualize Data: Build dashboards
  5. Set Up Alerts: Anomaly detection

Output

Successful execution produces:

  • Usage summary statistics
  • Daily breakdown reports
  • Top user analysis
  • Anomaly detection alerts
  • Exportable CSV data

Error Handling

See {baseDir}/references/errors.md for comprehensive error handling.

Examples

See {baseDir}/references/examples.md for detailed examples.

Resources

More by jeremylongshore

View all
rabbitmq-queue-setup
1,004

Rabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.

model-evaluation-suite
1,004

evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".

neural-network-builder
1,004

building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").

oauth-callback-handler
1,004

Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.