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add-analytics-instrumentation — Claude Code skill: how to install

Runs an end-to-end analytics workflow across PRs, branches, or files to detect trackable user actions and generate a ready-to-use tracking plan.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add amplitude/mcp-marketplace --skill add-analytics-instrumentation -a claude-code
Open on GitHub

What add-analytics-instrumentation does

The skill acts as the main orchestrator for Amplitude event tracking. It figures out what input you provided: a GitHub PR link, branch name, file path, or plain text feature description. When inspecting files directly, it scans source files for calls like track, logEvent, or Amplitude SDK imports and builds an initial change summary.

Next, it invokes discover-event-surfaces using the generated change brief. Once candidate events are identified, it triggers instrument-events. That step outputs a trackingPlan JSON file with precise file paths, surrounding functions, and property payloads for critical priority 3 events.

If your repository contains .amplitude/instrumentation-agent.yaml, the skill routes events to the designated project appId. Git errors or an empty candidate list will halt execution immediately, so running it against purely internal refactors without user-facing changes yields no tracking plan.

What it helps with

  • generate an analytics tracking plan for a pull request or branch
  • scan existing code for missing Amplitude event calls
  • extract event properties and produce structured trackingPlan JSON
  • map detected events to Amplitude project IDs from configuration

Example prompts for the agent

  • Instrument PR #42
  • Add analytics tracking to this branch feature/checkout
  • What analytics does src/components/Checkout.tsx need?

How to install add-analytics-instrumentation

1Skills CLI (Vercel)

npx skills add amplitude/mcp-marketplace --skill add-analytics-instrumentation -a claude-code

2Manually: put the files in place

~/.claude/skills/add-analytics-instrumentation/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/add-analytics-instrumentation/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about add-analytics-instrumentation

How do I install add-analytics-instrumentation in Claude Code?

Run npx skills add amplitude/mcp-marketplace --skill add-analytics-instrumentation -a claude-code. This places the skill manifest into ~/.claude/skills/add-analytics-instrumentation/SKILL.md.

What does add-analytics-instrumentation do?

It automates the tracking workflow by analyzing code changes, discovering user-facing event surfaces, and producing a concrete trackingPlan JSON with code locations and properties.

How does the skill handle multiple Amplitude projects?

When an .amplitude/instrumentation-agent.yaml file exists in the repository, the skill assigns events to specific appId values and flags cross-project tracking in the final review.

Source text

SKILL.md · MIT
---
name: add-analytics-instrumentation
description: >
  End-to-end analytics instrumentation workflow for a PR, branch, file,
  directory, or feature. Reads the code, discovers what events should be
  tracked, and produces a concrete instrumentation plan — all in one shot. Use
  this skill whenever a user wants to add analytics to a PR, asks "instrument
  this PR", "add tracking to this branch", "what analytics does this file need",
  "instrument the checkout flow", "run the full instrumentation workflow", or any
  request that implies going from code changes to a tracking plan. Also trigger
  when the user gives you a PR link, branch name, file path, or feature
  description and mentions analytics, events, or instrumentation. This is the
  main entry point for the analytics workflow — prefer it over calling the
  individual steps (diff-intake, discover-event-surfaces, instrument-events)
  separately.
---

# add-analytics-instrumentation

You are the orchestrator for the analytics instrumentation pipeline. Your job is
to figure out what the user wants to instrument, gather the relevant code, and
run the pipeline to produce a tracking plan.

## Pipeline

### Step 0: Capture intent

Before running anything, determine **what** the user wants to instrument. There
are four input types — infer the type from what the user has already provided in
the conversation. Only ask if it's genuinely ambiguous.

| Input type           | How to recognize it                                                            | Example                                                                     |
| -------------------- | ------------------------------------------------------------------------------ | --------------------------------------------------------------------------- |
| **PR**               | A PR URL, PR number, or phrases like "this PR", "my PR"                        | `instrument PR #42`, `https://github.com/org/repo/pull/42`                  |
| **Branch**           | A branch name or "this branch", "my branch", "current branch"                  | `instrument feature/checkout`, `add tracking to this branch`                |
| **File / Directory** | A file path, directory path, or glob pattern                                   | `instrument src/components/Checkout.tsx`, `add analytics to src/payments/`  |
| **Feature**          | A natural-language description of functionality, not a specific code reference | `instrument the onboarding flow`, `add tracking to the checkout experience` |

**Inference rules:**
- If the user provided a URL or `#number` → **PR**
- If the user provided something that looks like a branch name (contains `/`, no file extension, matches a git branch) → **Branch**
- If the user provided a path that exists on disk (file or directory) → **File / Directory**
- If none of the above match and the input is descriptive → **Feature**
- If the conversation already contains a PR link, branch name, or file path from earlier messages, use that instead of asking again

**If ambiguous**, ask the user:

> What would you like to instrument?
> 1. A specific file or directory
> 2. A PR
> 3. A branch
> 4. A feature (describe it and I'll find the relevant code)

Once you know the input type, proceed to the appropriate step:

- **PR or Branch** → go to Step 1 (diff-intake)
- **File / Directory** → go to Step 1a (direct file read)
- **Feature** → go to Step 1b (feature search)

### Step 1: diff-intake skill (PR or Branch)

Invoke the `diff-intake` skill with the user's PR or branch reference.

It produces a `change_brief` YAML block.

Capture the full YAML output — step 2 consumes it verbatim. Skip to Step 2.

### Step 1a: Direct file read (File / Directory)

Skip diff-intake entirely — there's no diff to analyze. Instead, build the
`change_brief` YAML yourself by reading the files directly.

1. **Resolve the input.** If a directory, find all source files in it (skip
   tests, config, lock files, generated code). If a single file, just use that.
2. **Read each file** and summarize what it does — focus on user-facing behavior,
   not implementation details.
3. **Scan for existing instrumentation** using the same patterns as diff-intake:
   `track(`, `trackEvent(`, `logEvent(`, `amplitude.track(`, `ampli.`, and
   analytics-related imports.
4. **Build the `change_brief` YAML** with `analytics_scope: high` (the user
   explicitly asked to instrument these files, so assume they want tracking).
   Set `primary: feat` and `classification.types: [feat]`. Populate
   `file_summary_map` with each file's summary, layer, and existing
   instrumentation.

Proceed to Step 2 with the YAML you built.

### Step 1b: Feature search (Feature)

The user described a feature in natural language. Your job is to find the
relevant code, then build a `change_brief`.

1. **Search git commit history** to find related commits. Use `git log --all --grep="<patterns>"`. This will find relevant commits. Then read the git commit body to understand the feature and relevant files. If the results are good, then proceed to generating the `change_brief` YAML
2. **Search the codebase** for files related to the described feature. Use a
   combination of:
   - Grep for keywords from the feature description (component names, route
     paths, function names, domain terms)
   - Glob for likely file paths (e.g., `**/checkout/**`, `**/onboarding/**`)
   - Read route definitions, navigation configs, or index files to find entry
     points
3. Build the `change_brief` YAML.

Proceed to Step 2 with the YAML you built.

### Step 2: discover-event-surfaces

Invoke the `discover-event-surfaces` skill, passing the `change_brief` YAML
from step 1.

It produces an `event_candidates` YAML block. If there are zero candidates,
stop and tell the user the change has user-facing impact but no events worth
instrumenting were identified.

If event_candidates is empty, stop here and tell the user there's nothing to
instrument.

Capture the full YAML output — step 3 consumes it.

### Step 3: instrument-events

Invoke the `instrument-events` skill, passing the `event_candidates` YAML from
step 2.

It produces a `trackingPlan` JSON with exact file locations, tracking code, and
property definitions for every critical (priority 3) event.

## Presenting the result

After step 3 completes, present the tracking plan to the user. Walk through each
event briefly:

- What it tracks and why it matters
- Which Amplitude project(s) it routes to (`appId`/`appIds`) — call this out when
  the repo has an `.amplitude/instrumentation-agent.yaml` and events span more
  than one project
- Where the tracking call goes (file + function)
- What properties it sends

Then ask if they want to adjust anything or proceed to implementation.

## Error handling

If any step fails (e.g., the PR doesn't exist, git commands error, no files to
analyze), surface the error clearly and stop. Don't try to continue with
incomplete data.

© amplitude · MIT · amplitude/mcp-marketplace

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