智能体工作流

custom-metrics

launchdarkly/ai-tooling

Create, track, retrieve, update, and delete custom business metrics for configs. Covers full lifecycle: define metrics with the LaunchDarkly MCP server, emit events via SDK, and query results.

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安装命令

npx skills add https://github.com/launchdarkly/ai-tooling --skill custom-metrics

SKILL.md

Custom Metrics for Configs

Full lifecycle management of custom business metrics: create metric definitions with the MCP server, track events via SDK, retrieve metric data, and manage metrics programmatically.

Prerequisites

  • LaunchDarkly AI SDK initialized (see sdk-install). The SDK reads LD_SDK_KEY from the environment.
  • The remotely hosted LaunchDarkly MCP server configured in your environment
  • A LaunchDarkly API token with the writer role, only to update or delete metrics, or for scripts that can't use MCP
  • Understanding of built-in agent metrics (see built-in-metrics)

API Access

Use the LaunchDarkly MCP server tools wherever one exists: they authenticate via OAuth and need no token. Create metrics with create-metric, check that events are arriving with list-metric-events, and read definitions with get-metric and list-metrics. The MCP server can't update or delete metrics, so those steps use the REST API. For them, or for scripts that can't use MCP, ask the user to paste an API access token for this session and substitute it for {api_token} in the examples below. Do not search environment variables, .env files, or agent/MCP config files for credentials.

Metrics Lifecycle Overview