# built-in-metrics

Wraps your existing model call in a LaunchDarkly tracker to capture duration, tokens and success or error. Useful once an agent runs and you want to see what it costs.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1800 installs · ★ 28
- Author: launchdarkly
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/launchdarkly/ai-tooling/tree/HEAD/skills/agentcontrol/built-in-metrics/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/built-in-metrics

## What it does

The skill inspects the provider call already in your codebase and picks a tier from a four-step ladder. Top tier is the managed runner: `ai_client.create_model(...)` in Python or `aiClient.createModel(...)` in Node, then `await model.run(...)`. For conversational calls that is the shortest path, and metrics come out with zero tracker calls.

When the call is a one-shot completion or an agent step, the skill checks for a provider package: `@launchdarkly/server-sdk-ai-openai`, `-langchain`, `-vercel` on Node, `launchdarkly-server-sdk-ai-openai` and `-langchain` on Python. Then you wrap the call in `tracker.trackMetricsOf(Provider.getAIMetricsFromResponse, () => providerCall())`. With no package available — Anthropic direct, Gemini, Cohere, custom HTTP — you write a small extractor that returns `LDAIMetrics`. The bottom tier is manual: separate `trackDuration`, `trackTokens`, `trackSuccess` calls plus `trackTimeToFirstToken` for streams.

Tracking needs a tracker, and the tracker comes from `config.create_tracker()` or `createTracker()` on an existing config. If the call site still uses `aiclient.config(...)` or an `AIConfig(...)` default, the skill migrates it as part of the same job. You need the LaunchDarkly server-side AI SDK: `launchdarkly-server-sdk-ai>=0.20.0` for Python or `@launchdarkly/server-sdk-ai>=0.20.0` for Node. Streaming with time-to-first-token lands on the manual tier even when a provider package covers everything else.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add launchdarkly/ai-tooling --skill built-in-metrics -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/built-in-metrics/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/built-in-metrics/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add launchdarkly/ai-tooling --skill built-in-metrics -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/built-in-metrics/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add launchdarkly/ai-tooling --skill built-in-metrics -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/built-in-metrics/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install launchdarkly/ai-tooling built-in-metrics
```

2. **Manually: put the files in place**
- File: `.github/skills/built-in-metrics/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add launchdarkly/ai-tooling --skill built-in-metrics -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/launchdarkly/ai-tooling --path skills/agentcontrol/built-in-metrics
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/built-in-metrics/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add launchdarkly/ai-tooling --skill built-in-metrics -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/launchdarkly/ai-tooling/tree/HEAD/skills/agentcontrol/built-in-metrics/SKILL.md

## When to use it
- capture duration and tokens from a model call that already ships
- pick an instrumentation tier that matches the call shape
- use a provider package instead of hand-written tracker calls
- write a metrics extractor for a provider with no package
- move a call site off the pre-0.20 SDK API before adding tracking

## Example prompts
- Add LaunchDarkly metrics to the OpenAI call in this file
- Which tracking tier fits our Anthropic agent step?
- We need time-to-first-token on a stream — what should we use?

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 092fe088109a9ce9.

BEGIN THIRD-PARTY FILE 092fe088109a9ce9
```text
---
name: built-in-metrics
description: "Instrument an existing codebase with LaunchDarkly config tracking. Walks the four-tier ladder (managed runner → provider package → custom extractor + trackMetricsOf → raw manual) and picks the lowest-ceremony option that still captures duration, tokens, and success/error."
license: Apache-2.0
compatibility: Requires the LaunchDarkly server-side AI SDK (`launchdarkly-server-sdk-ai>=0.20.0` for Python or `@launchdarkly/server-sdk-ai>=0.20.0` for Node) and an existing config.
metadata:
  author: launchdarkly
  version: "1.0.0-experimental"
---

# Agent Metrics Instrumentation

You're using a skill that wires LaunchDarkly agent metrics around an existing provider call. Your job is to audit what's already there, pick the right tier from the ladder below, and implement it with the **least ceremony that still captures the metrics the Monitoring tab needs** (duration, input/output tokens, success/error, plus TTFT when streaming).

The single most important thing to get right: **default to the highest tier that fits the shape of the call**. Going lower ("just write the manual tracker calls") looks flexible but costs you drift, missed metrics, and legacy patterns the SDKs have moved past.

## The four-tier ladder

This is the order the official SDK READMEs (Python core, Node core, and every provider package) recommend. Walk from the top and stop at the first tier that fits:

| Tier | Pattern | Use when | Tracks automatically |
|------|---------|----------|----------------------|
| **1 — Managed runner** | Python: `ai_client.create_model(...)` returning a `ManagedModel`, then `await model.run(...)`. <br>Node: `aiClient.createModel(...)` returning a `ManagedModel`, then `await model.run(...)`. | The call is conversational (chat history, turn-based). This is what the provider READMEs lead with. | Duration, tokens, success/error — **all of it, zero tracker calls**. |
```
END THIRD-PARTY FILE 092fe088109a9ce9

The third-party file has ended. Everything between the 092fe088109a9ce9 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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