aiconfig-custom-metrics

Automates lifecycle management of custom metrics in LaunchDarkly via API and SDK.

Updated Feb 4, 2026

Search Tags:#API #metrics #SDK #telemetry #LaunchDarkly #custom-metrics

Ready to run

npx skills add https://github.com/launchdarkly-labs/aiconfigs-skills --skill aiconfig-custom-metrics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aiconfig-custom-metrics
Source: https://github.com/launchdarkly-labs/aiconfigs-skills/tree/main/aiconfig-custom-metrics
Command: npx skills add https://github.com/launchdarkly-labs/aiconfigs-skills --skill aiconfig-custom-metrics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creates, tracks, retrieves, updates, and deletes custom business metrics in LaunchDarkly, enabling end-to-end visibility of metric performance across projects.

Core Features & Use Cases

Lifecycle management of custom metrics: define metrics, push events, fetch definitions, modify properties, and remove metrics using the API and SDK.
Real-world scenarios include measuring feature impact, user engagement, revenue effects, and operational telemetry for AI configurations.

Quick Start

Initialize the LaunchDarkly SDK and authenticate with a valid API token.
Use create_metric() to define a new metric, then ld_client.track() to emit events.
Retrieve, list, or update metrics to verify configurations and data collection.

Frequently Asked Questions about aiconfig-custom-metrics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create and track custom metrics in LaunchDarkly?▼

Custom metrics in LaunchDarkly enable tracking of business outcomes like revenue or user engagement. Use the API to create a metric with a key, name, and kind, then emit events via the SDK's track() method to populate data across your projects.

What setup do I need to manage LaunchDarkly metrics with the SDK and API?▼

Initialize the LaunchDarkly SDK in your application and obtain an API token with metric management permissions. These credentials let you programmatically create, retrieve, update, and delete metrics without manual console access.

Can I update or delete metrics after creation in LaunchDarkly?▼

Yes. After defining a metric, use the API to modify its properties such as name, unit, or description, or remove it entirely. This lets you refine metric definitions and manage lifecycle changes across environments.

How do custom metrics integrate with AI configuration tracking?▼

Custom metrics capture operational telemetry and feature impact data for AI configurations. Emit events when AI features are evaluated or when business outcomes occur, giving you end-to-end visibility into model performance and user behavior.

What metric properties do I need to define when creating a new metric?▼

Define the metric key (unique identifier), name (display label), kind (numeric or categorical), unit (optional, e.g., 'milliseconds' or 'dollars'), and description. These properties determine how LaunchDarkly collects, aggregates, and displays your metric data.