Name: launchdarkly-metric-create
Author: launchdarkly

Create a LaunchDarkly metric that measures what matters for an experiment or rollout. Use when the user wants to create a metric, track an event, measure page views, button clicks, conversion, latency, error rate, or any custom numeric or binary outcome. Instruments the event first when needed (incl

LaunchDarkly Metric Create You're using a skill that will guide you through creating a LaunchDarkly metric. For custom metrics, getting events flowing comes first — before the metric is created. Your job is to determine the right metric kind, instrument the event if it isn't already flowing (including SDK setup and environment wiring), check for duplicates, propose a metric config, get explicit confirmation, then create and verify. Prerequisites This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment. Required MCP tools: create metric — create the metric get metric — verify it after creation get environment — fetch the client side SDK key when instrumenting Optional MCP tools (enhance workflow): list metrics — check for existing metrics with the same event key and understand naming conventions list metric events — discover which event keys have recent activity before committing to one (custom metrics only) Two Different "Projects" — Never Confuse Them Users work with two completely separate things that both get called "project." You must keep these distinct at all times: What it is How the user refers to it What you do with it LaunchDarkly project The project inside the user's LD account where the metric will be created Usually sounds like an environment or team name: my app , anthony agent dev 5000 , production Pass as projectKey to all MCP tool calls Local codebase The developer's application on disk that you'll instrument with a track() call Often a folder name, repo name, or app name: checkout proj , frontend , my react app Use to find and edit source files Rules for resolving these from user input: If the user says "my application at X" or "my codebase" or "my repo" → they mean the local codebase . X is a folder path or project name, not a LaunchDarkly key. If the user says "add it to X" or "in LaunchDarkly" or "my LD project" → they mean the LaunchDarkly project . X is the projectKey for API calls. A user can name their local codebase checkout proj while their LaunchDarkly project is anthony agent dev 5000 . These are unrelated. Never assume the local codebase name is a LaunchDarkly project key. If you're unsure which is which, ask directly: "Just to confirm — what's your LaunchDarkly project key? (This is different from your local app name — you can find it in the LD UI under Account Settings Projects.)" When both are needed (e.g. for a custom metric with instrumentation), confirm each explicitly before proceeding. Workflow Step 1: Determine the Metric Kind LaunchDarkly has three metric kinds. Choose the right one before anything else. Kind How events are collected Requires custom Developer calls ldClient.track(eventKey) in code eventKey pageview Fires automatically when a user visits a matching URL — no SDK call needed urls (URL match rules) click Fires automatically when a user clicks a CSS selector on a matching URL — no SDK call needed urls + selector Decision rules: User says "track when someone views a page / visits a URL" → pageview (preferred — no instrumentation required) User says "track when someone clicks a button / link" → click User says "track a custom event" or references a track() call → custom When pageview or click would work, suggest it over custom — it requires no code changes. Step 2: Resolve the Data Source For pageview and click metrics: Ask for the URL(s) to match. Confirm the kind of URL match rule: substring — URL contains this string (most common) exact — URL must match exactly canonical — matches the canonical URL regex — full regex pattern For click metrics, also ask for the CSS selector (e.g. .checkout btn , submit ). Skip list metric events — these metrics don't use event keys. Skip to Step 3. For custom metrics — check events first, instrument if needed: Call list metric events immediately to see which event keys are already flowing: Case A — the event key is already in the list: Confirm the key with the user and proceed to Step 3. No instrumentation needed. Case B — the event key is NOT in the list: The metric can't measure anything without events. Instrument the event now before creating the metric. Do not simply warn and ask whether to proceed — treat instrumentation as the default next action. Follow the instrumentation sub workflow below, then re check list metric events to confirm events are flowing before moving to Step 3. Only skip instrumentation if the user explicitly says they want to create the metric first and wire the event up later — in that case, remind them at the end that the metric will produce no data until the event is tracked. Step 2b: Instrument the Event (when events aren't flowing) This sub workflow gets a track() call into the codebase and connects the app to the right LaunchDarkly environment. Complete all steps before returning to the main workflow. 1. Find the right place in the codebase. Locate the function or handler where the event naturally occurs (e.g. a checkout submit handler, a form submission callback). Read the relevant source files to understand the existing structure before making changes. 2. Determine the event key. If the user hasn't specified one, propose a descriptive kebab case key that matches what the code is doing (e.g. checkout completed , signup submitted ). Confirm with the user before using it. 3. Fetch the client side SDK key. Ask the user which environment they want to connect to (e.g. "test", "production", "staging") — just the environment name. Then call: Use the clientSideId from the response. 4. Write the environment file. Check whether a .env file (or equivalent — .env.local , .env.development , etc.) already exists. If the file does not exist , create it. If the file exists and already contains the key (e.g. VITE LD CLIENT SIDE ID ), compare the stored value to the clientSideId returned by get environment . If they differ, surface the discrepancy to the user: "Your .env already has VITE LD CLIENT SIDE ID=<old , but get environment returned <new for the <env environment. Should I update it?" Do not silently keep the old value — a mismatched client side ID means events will be sent to the wrong project or environment. If the file exists but the key is absent, add it without touching other values. Use the variable name appropriate to the project's build tool (e.g. VITE LD CLIENT SIDE ID for Vite, REACT APP LD CLIENT SIDE ID for CRA, NEXT PUBLIC LD CLIENT SIDE ID for Next.js). 4b. Set the SDK base URL if the user is not on app.launchdarkly.com. The SDK defaults to app.launchdarkly.com for all traffic. If the user is on a different LaunchDarkly deployment (e.g. an internal staging environment like catamorphic, or a dedicated instance), events and flag evaluations will silently go to the wrong host. Detect this by inspecting any links or UI URLs in MCP API responses — if they point to a host other than app.launchdarkly.com , you are on a non production deployment. When in doubt, ask: "Are you connecting to app.launchdarkly.com or a different LaunchDarkly instance? (e.g. an internal or staging environment)" If they are on a non standard host, add three additional variables to the .env file: And pass them to the SDK options at init time: Omit the options block entirely if they are on app.launchdarkly.com — the defaults are correct and no extra config is needed. 5. Install and initialize the SDK if it isn't already present. Check package.json (or the equivalent dependency file) for an existing LD SDK. If none is found, install the right one for the project's stack: React → launchdarkly react client sdk Browser JS → launchdarkly js client sdk Node.js server → @launchdarkly/node server sdk Initialize the SDK at the app's entry point (e.g. wrap the React root with LDProvider , configure LDClient.init() in the server entry, etc.). Pass the client side ID from the env file. Use an anonymous user/context as the default unless the app already manages user context. 6. Add the track() call. In the location identified in step 1, add the call immediately before or after the action completes: Count / occurrence metric: ldClient.track('event key') Value metric: ldClient.track('event key', null, numericValue) Use optional chaining ( ldClient?.track(...) ) in client side code where the client may not yet be initialized. 7. Verify events are flowing. After the instrumentation changes are made, remind the user to run the app and trigger the event at least once. Then call list metric events again to confirm the key appears before proceeding to metric creation. Step 3: Check for Existing Metrics Before creating anything, use list metrics to scan the project: 1. Check for duplicates. Search for metrics with the same event key, URL pattern, or similar names. Avoid creating a second metric that measures the same thing — instead, flag the existing metric and ask the user if they want to reuse it. 2. Learn the naming convention. Are metric keys kebab case or snake case ? Are there common tag patterns? Match what already exists. 3. Understand the tag taxonomy. Tags like team:growth , area:checkout , or type:guardrail may already exist. Suggest relevant tags based on what the user describes. Step 4: Propose the Metric Configuration Before calling any API, surface a proposed configuration in plain language for the user to confirm or edit. Determine measure type. The right choice depends on what the user is trying to learn and how they'll use the metric — in an experiment, a guarded rollout, or a release policy. Do not assume. When the event is something a user can do repeatedly (click, add to cart, view page, etc.), always ask before proposing: "Are you trying to measure how many times this event happens in total ( count ), or what percentage of users triggered it at least once ( occurrence )?" Tie the question to their context: Experiments — occurrence is common for conversion goals (did the treatment cause more users to do X?); count is better for engagement or volume goals (did the treatment cause more total actions?) Guarded rollouts / release policies — occurrence is typical for error rate guardrails (what fraction of users hit an error?); count suits absolute volume guardrails (total error events) If the user explicitly says "percent of users" or "conversion rate" → occurrence If the user explicitly says "number of times" or "total events" → count Only skip asking if the intent is unambiguous from context (e.g. "API latency" → value , "error rate" → count , "signup conversion" → occurrence ). What the user wants to measure Measure type Means Total times the event occurred count Raw event count per analysis unit Whether each user triggered the event at all occurrence Conversion / binary (did it happen?) A numeric value attached to the event value Latency, revenue, score, etc. Determine success criteria: Higher is better → HigherThanBaseline (conversion rate, revenue, engagement) Lower is better → LowerThanBaseline (latency, error rate, bounce rate) Use common templates as defaults when the user's intent is clear: User intent kind measure type success criteria unit Page visit / view rate pageview occurrence HigherThanBaseline — Button / link click rate click occurrence HigherThanBaseline — API latency / page load time custom value (average) LowerThanBaseline ms Signup / conversion rate custom occurrence HigherThanBaseline — Error co