Hire an AI analytics events and tracking gap review

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A tracking plan with events, naming rules and one main metric
• Setup scores out of 100 for analytics and paid social, against last month
• Every planned event that stopped firing or changed name, with the fix
• Double counts found, with the fix
• The link register updated, with untagged campaigns marked
• Each platform's claim against your real total, a truer return and a trust level

How it does the job well

• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.
• Compare like with like: same period length, definitions and segments.
• Separate what the data shows from what it might mean, and give confidence levels.

Mistakes it avoids

• Treating a platform's reported return as the truth.
• Renaming an event mid-month and splitting the chart.
• Fixing a tag without re-checking it the next month.
• Launching a campaign without a tagged link, then blaming "direct".
• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.

What a good result looks like

An example, not your task: A chart went to zero last month and nobody noticed for weeks.

• This month in one line. Setup scores against last month, and the gap between platform claims and your real totals.
• Broken first. Events with zero hits, double counts and campaigns that ran untagged, each with a likely cause.
• Analytics setup. Missing settings and renamed events, with a fix for each.
• Paid social tracking. The tracking route, whether each event counts once, and the fix, if paid social runs.
• Link register. Each campaign's tagged link, with gaps marked.
• Claims against reality. Each platform's claim next to your store total, with a trust level.

How the work is checked

• Every event checked is in plan.md, and the plan did not change.
• Last month's fixes were re-checked before new ones were proposed.
• The platform claims and the store totals cover the same dates.
• Every broken item has a fix and the person who can make it.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Write the plan once. The event list, naming rules and one main metric that every month is checked against.
• Read last month. Picks up the scores and the fixes marked done, to re-check first.
• Check the analytics setup. Missing settings, events that stopped or were renamed, and a score out of 100 against last month.
• Check the paid social tracking. The server route, proof each event counts once or the double counts found, and a score.
• Check the links when campaigns launched. Tagged links against the register, with any untagged campaign marked as a gap.
• Check the claims against reality. Each platform's conversions and revenue against your store totals for the same dates, a truer return and a trust level.
• Set up AI traffic. On the first run or when an AI assistant starts sending visits, the channel, post-purchase question and redirect list.
• Log and hand back. Updates the health and link files, then stops for your yes.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id monthly-tracking-health-keeper only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/monthly-tracking-health-keeper/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Which analytics and ad platforms you use, how tags are installed and which events you track
• Last month's platform reports and your store or billing totals for the same dates
• Your campaign link register (advised)
• What changed last month, such as theme updates, new apps or new campaigns (advised)
• Your tracking plan, after the first run

Helpful context (optional)

• A screenshot of each platform's conversion settings
• Your developer's release notes for the month

Tools and access

• Claude Code: It can keep the plan, health and link files between months and run on the first working day.
• Your tag manager: Export the container or list the tags, so the setup check knows what should fire.
• Google Analytics 4: The property being checked. Admin, DebugView and Realtime are used.
• Google Tag Manager: Its preview mode shows whether a tag fired before you publish.
• Meta Events Manager: Shows browser and server events, event IDs, the test events screen and event match quality.
• Google Tag Manager (web and server): Preview mode shows whether tags fired, and the server container shows the tag status.
• Shopify marketing area: Where the match rules, activities and attribution models are set.
• Google Analytics 4: A second view of the same tagged traffic.
• Any good chat AI: Any current chat AI can build the link sheet from your channel list.
• Any good chat AI: Any chat AI can run the ladder and the score. Check the store base by hand.
• Meta Ads Manager: Lets you compare attribution settings and break results down by new, engaged and existing audiences.
• Shopify: Gives new-customer revenue and orders, and a cohort report for your own repeat data.
• A post-purchase survey app: Collects where buyers say they first heard of you, so you can compare it with platform data.
• GA4: Free. Holds the custom channel group and the traffic acquisition report.
• Google Tag Manager: A no-developer way to add store events if your platform's own events are missing.
• Shopify: Your order status page or a post-purchase survey app can hold the survey question.
• Ahrefs Web Analytics: May have a built-in AI search channel and may split unknown traffic from direct. Check this in the tool.

Functions and deliverables

• Analytics events and tracking gap review: Review event reports, campaign links and platform totals. Find missing evidence, double counts and tag changes to check.
  • A tracking plan with events, naming rules and one main metric
  • Setup scores out of 100 for analytics and paid social, against last month
  • Every planned event that stopped firing or changed name, with the fix
  • Double counts found, with the fix
  • The link register updated, with untagged campaigns marked
  • Each platform's claim against your real total, a truer return and a trust level
• Event tracking planner: Turn product steps and set goals into named actions, user rules, and a plan to track use.
  • Event list with names and properties
  • Naming and versioning rules
  • Funnel and retention definitions
  • User identity plan
  • North Star metric choice
  Source: RampStack.
• GA4 store setup checker: Check your GA4 store setup in one pass. Get the day-one settings you missed, the store events to add and a setup score out of 100.
  • A pass or fail list of day-one settings with click paths
  • The store events that are missing
  • A key event plan that includes your thank-you page
  • A test sequence, the wait time before judging data, and a score out of 100 with its sum
• Meta server tracking checker: Share your tracking route and test records. Compare setup options, check duplicate events and identify consent or test-code gaps.
  • A recommended route with the reason, the realistic gain and the monthly cost to confirm
  • Setup steps in order
  • A pass or fail on matching event IDs
  • Fixes for missing IDs, consent rules and test-ID leaks, a weekly check, and a score out of 100
• Shopify campaign link planner: Share campaign dates, channels and page links. Get tagged links, Shopify match rules and an attribution model.
  • Naming rules and a link sheet with tags for each channel
  • The auto-match rules to enter in Shopify's marketing area
  • The attribution model to start with, and why
  • A weekly review routine
• Ad return evidence checker: Share ad reports and store totals. Get matched comparisons, evidence gaps and a custom review score, without a causal sales claim.
  • A comparison of reported measures, definitions, periods and missing coverage
  • A supported top-ad breakdown with audience and attribution limits
  • The evidence basis and next check for each proposed budget decision
  • A bounded custom checklist score with missing checks shown
• AI traffic tracking planner: Give it your analytics and checkout. Get an AI traffic channel, a post-purchase question and a redirect list.
  • A custom AI traffic channel with its source rules
  • A tracking sheet: AI sessions, orders, revenue and pages hit
  • A survey question with answer options and where to place it
  • A redirect list for AI-made 404s
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.

Tasks you can use it for

• A chart went to zero last month and nobody noticed for weeks.
• Your ad platforms together claim more sales than your store recorded.
• You launch campaigns often and "direct" traffic keeps growing.

Limits to check before starting

• You have no analytics tags or paid platforms yet. Set up one basic tracking tool first, or ask a developer to add it.
• You have one setup question, such as a GA4 store check. Use a single GA4 setup checker, not a monthly review.
• You want to watch product funnel drops each week. Use the Product funnel steps and experiment review.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Start a chat with the agent. In Claude Code, add it as a skill or save it in .claude/agents, then ask it to run last month.
• Describe your setup once. On the first run, approve the tracking plan it writes.
• Each month, export each platform's report and your store totals for the same dates. Put them in one folder, or attach them in the chat.
• Say what changed last month. Read the broken items first, then the fix list.
• Make the fixes you agree with and have someone fire a test event. Run it again next month; it re-checks what you fixed.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/monthly-tracking-health-keeper for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.