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    Website Analytics Tracking QA Agent

    1

    Audit website analytics implementation by comparing tracking specifications against actual event logs and data samples.

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    Website Analytics Tracking QA Agent

    Website Analytics Tracking QA Agent

    Example session with this skill installed

    I've attached our GA4 tracking plan and a CSV of event logs from our staging environment. Please audit the 'purchase' and 'add_to_cart' events for naming consistency and required parameters.

    • Read your context and instructions
    • Compiled the website analytics tracking
    • Generated the data export

    I have reviewed the GA4 tracking plan against the staging event logs provided. The audit identified a naming mismatch for the cart event and a missing required parameter in the purchase event logs.

    Analytics QA Audit Report

    Audit Scope and Evidence Reviewed

    • Specification: GA4 Tracking Plan (purchase, add_to_cart)
    • Evidence: staging_event_logs.csv (245 rows)
    • Platform: Google Analytics 4

    Event Coverage Comparison

    Expected eventObserved matchRequired parametersEvidenceStatusNotes
    add_to_cartAddToCartitem_id, valueRow 12, 45, 89MismatchCase sensitivity issue
    purchasepurchasetransaction_id, valueRow 102Partialtransaction_id missing in sample

    Findings Register

    IDPriorityClassificationLocation/eventEvidenceIssueRecommended fixRetest
    FIND-001P2Confirmed mismatchadd_to_cartstaging_event_logs.csvObserved event uses PascalCase (AddToCart) instead of snake_case.Update trigger to fire add_to_cart.Verify event name in GTM Preview.
    FIND-002P1Confirmed mismatchpurchaseRow 102, 115transaction_id is null in all observed purchase events.Map the order ID from the data layer to transaction_id.Confirm parameter populates in BigQuery/GA4 Debug.

    Prioritized Remediation Plan

    1. Immediate (P1): Fix the purchase event transaction_id to ensure conversion deduplication.
    2. High (P2): Align AddToCart naming with GA4 standards to fix broken reporting in standard reports.

    Next steps

    • Share the Naming and Parameter Review with the frontend team to correct the event casing.
    • Provide a data layer snippet from the success page to debug the missing transaction_id.
    • Use the Retest Checklist provided below once the fixes are deployed to staging.

    staging_event_logs.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Analytics implementations often fail due to naming drift, missing parameters, or broken triggers that go unnoticed until reporting breaks. This skill automates the tedious process of cross-referencing tracking plans against actual event logs. It acts as a specialized QA layer for analytics specialists and growth teams, ensuring data integrity before it reaches the dashboard.

    What it does

    • Compare specifications against event logs, data-layer samples, or tag-manager exports to find discrepancies.
    • Identify inconsistencies in naming conventions, capitalization, and parameter aliases across platforms.
    • Flag missing parameters and type mismatches that could invalidate core conversion tracking.
    • Detect tracking gaps in specific user journeys or funnels based on supplied evidence.
    • Prioritize findings using a P1-P4 severity scale based on business impact and evidence strength.

    How it works

    1. Input Collection: Provide your tracking plan/specification and evidence sources like event logs or implementation notes.
    2. Normalization: The skill extracts expected events and observed data into a unified schema.
    3. Automated Audit: It runs comparisons to find undocumented events, missing triggers, or duplicate firing patterns.
    4. Report Generation: It produces a prioritized findings register with specific remediation steps and a retest checklist.

    Frameworks & tools

    Works with exports and specifications from Google Analytics 4 (GA4), Segment, Mixpanel, Amplitude, Google Tag Manager (GTM), and custom data layers.

    Why this beats prompting it yourself

    Generic prompts often hallucinate that a tracking issue is "confirmed" when evidence is merely missing. This skill follows strict logic to distinguish between confirmed mismatches, potential issues, and verification gaps, preventing false positives in your QA reports.

    Use cases

    • Pre-deployment QA to verify a new analytics implementation matches the developer handoff doc.
    • Audit existing setups by comparing historical tracking plans against recent event exports.
    • Funnel validation to ensure every step of a checkout or signup flow is correctly instrumented.
    • Parameter audits to check if required metadata like currency or transaction_id is consistently present.

    Known limitations

    Cannot access live websites or analytics accounts directly. Requires users to supply event logs or exports for analysis. Does not estimate revenue loss or conversion lift.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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