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Analytics

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ericrisco
analytics

Use when instrumenting product or web analytics — GA4/PostHog SDK wiring, event taxonomy, funnels, double-counted events, consent gating, PII scrubbing. NOT charting that data (that is dashboard), NOT choosing which metrics matter (that is kpi-framework), NOT experiment math (that is ab-testing), NOT cookie-policy text (that is gdpr-privacy).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill nameanalytics
Stars
239
Forks
24
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ericrisco on GitHub. Read the source before you install it.

Installation

Install the Analytics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ericrisco/rsc-harness/tree/main/skills/analytics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/analytics .claude/skills/analytics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analytics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analytics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analytics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analytics — the instrumentation layer

This skill owns the capture side of analytics: deciding what to track, how to name it, where the SDK lives in the codebase, and how not to leak PII or break consent law. It produces three checkable artifacts — an event taxonomy, tracking code (GA4 and/or PostHog), and a consent wiring. Everything downstream of capture (charts, KPI choice, experiment stats, raw-event SQL, legal text) belongs to a sibling; see the routing table below.

The order of work is fixed: taxonomy → SDK wiring → consent gate → PII scrub → funnel + validation.

When NOT to use

The askRoute to
Chart the captured data on a boarddashboard
Decide which metrics matter (North Star, AARRR)kpi-framework
Scheduled stakeholder reports / exportsreporting
Variant assignment, significance, experiment designab-testing (PostHog experiments live there; PostHog event capture lives here)
Query a warehouse of raw events with SQLclickhouse-analytics / duckdb / sql
App error/trace/uptime telemetry (Sentry, OpenTelemetry)observability
Cookie-banner legal text, DPA, ROPA, subject rightsgdpr-privacy / data-policy
Predict future values from a seriesforecasting

The load-bearing line: analytics = events flow in; dashboard/reporting = events flow out.

Decision: GA4 vs PostHog vs both

You needPick
Web/ads attribution, Google Ads conversions, marketing audiencesGA4
Product behavior, funnels, feature flags, session replay, self-serve insightsPostHog
Both marketing attribution and deep product analytics (very common)Both — GA4 for ads, PostHog for product

Running both is normal and fine. Keep one taxonomy shared across both so a purchase means the same thing everywhere. Do not let the two tools drift into two naming schemes.

Step 1 — Event taxonomy first, code second

An event name is a contract: design the taxonomy before you write a single SDK call, and never rename a live event in production. Every funnel, audience, dashboard, and saved insight downstream is keyed by the exact event name and property keys. Rename signup_completed to sign_up after launch and you silently fork the metric into two — the old funnel flatlines, the new one starts from zero, and nobody notices for a week. You can add events forever; you can never safely rename one.

Name events object_action in snake_case: signup_completed, checkout_started, invoice_paid. The object is the noun, the action is a past-tense verb. Detail goes in properties, never in the name — cta_clicked with { location: "navbar" }, not three events navbar_cta, hero_cta, footer_cta.

GA4 hard constraints (the SDK silently truncates or drops violators): event names ≤ 40 chars, alphanumeric + underscore only, must start with a letter; ≤ 25 params per event; ≤ 25 user properties. Prefer GA4 recommended events — sign_up, login, purchase, add_to_cart, search, generate_lead — with their prescribed params, because they unlock prebuilt reports and audiences you cannot get from a custom name.

text
Bad                              Good
"Clicked The Big Button"    →    cta_clicked          { location: "hero" }
trackSignup_v2              →    signup_completed     { method: "google" }
purchaseEvent2              →    purchase             { value: 49, currency: "EUR" }
NavbarCheckoutButton        →    checkout_started     { source: "navbar" }

Identify vs anonymous. Before login the user is anonymous (client_id / distinct_id). On authentication, call identify(stableUserId, { plan, signup_date }) — the stable id is your DB user id, a UUID, never the email. On logout call reset() so the next visitor on a shared machine does not inherit the previous person. The full starter SaaS + e-commerce catalog and property conventions are in references/event-taxonomy.md.

Step 2 — Wire the SDK

GA4 with the global site tag (Next.js Script shown; the consent block in Step 3 must run before this):

html
<script async src="https://www.googletagmanager.com/gtag/js?id=G-XXXXXXXXXX"></script>
<script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){ dataLayer.push(arguments); }
  gtag('js', new Date());
  gtag('config', 'G-XXXXXXXXXX');
</script>

PostHog (posthog-js) — the cost/privacy-correct defaults:

ts
import posthog from 'posthog-js';

posthog.init('phc_xxx', {
  api_host: '/ingest',              // reverse proxy: first-party path beats ad-blockers
  ui_host: 'https://eu.posthog.com',
  person_profiles: 'identified_only', // no profile per anonymous visitor — cheaper, more private
  defaults: '2025-05-24',
  // autocapture: false,            // turn off if you want a deliberate, named-only taxonomy
});

// on login:  posthog.identify(user.id, { plan: user.plan });
// on logout: posthog.reset();

person_profiles: 'identified_only' is the recommended default — it avoids creating a person profile for every anonymous visitor. A reverse proxy (serving the SDK + ingestion under a first-party path like /ingest) is standard practice for both PostHog and GA to dodge ad-blockers and tracking-prevention.

Server-side capture for actions off the browser — payment confirmation, webhooks, cron. With @posthog/next, await getPostHog() works in server components, route handlers, and server actions; it reads identity from the PostHog cookie (and opts the route into dynamic rendering, since it calls cookies()). GA4 server events use the Measurement Protocol with the client_id. Full snippets — gtag install, Consent Mode v2, Measurement Protocol, recommended-event param tables — are in references/ga4-setup.md and references/posthog-setup.md.

Step 3 — Consent before collection

Decision: do you serve EEA / UK / CH traffic? If yes, Consent Mode v2 is not optional. Since 21 July 2025 Google enforces it for EEA/UK traffic: tags without connected consent signals lose conversion tracking, remarketing, and demographics. Four params are required and default to denied for EEA/UK/CH:

html
<!-- This block MUST run BEFORE the gtag('config', ...) call in Step 2. Order is load-bearing. -->
<script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){ dataLayer.push(arguments); }
  gtag('consent', 'default', {
    ad_storage: 'denied',
    ad_user_data: 'denied',
    ad_personalization: 'denied',
    analytics_storage: 'denied',
    wait_for_update: 500,
  });
  // when the banner is accepted:
  // gtag('consent', 'update', { analytics_storage: 'granted', ad_storage: 'granted', ... });
</script>

PostHog's equivalent is posthog.optOut() / posthog.optIn() — start opted-out for EEA visitors and opt in on acceptance. The legal text of the banner (what it says, the DPA, retention) is gdpr-privacy's job; this skill only wires the signal the banner emits. Region-scoped defaults live in references/ga4-setup.md.

Step 4 — PII discipline

Never pass these into a capture( / gtag('event' / track( call. They turn an analytics store into a breach-reportable PII store and violate most processing agreements:

Banned in event propsAllowed instead
email, phone, full namea hashed id, or set on the person profile only — not on every event
raw IP, geolocation coordslet the SDK derive coarse geo server-side
password, token, secret, API keys, session_idnothing — these never belong in analytics
credit_card, ssn, IBANnothing

Scrub at the boundary — a single capture() wrapper that strips known PII keys is far safer than trusting every call site. A GA4 user_id is a stable opaque identifier, not an email; sending an email as the user_id is a PII leak and a violation of Google's policy.

Step 5 — Funnels & validation

Define the funnel from the named events, in order, e.g. signup_started → signup_completed → project_created → invoice_paid. The funnel is only as reliable as the names, which is why Step 1 comes first.

Before you ship, validate — do not trust that it works:

  • GA4: open the DebugView (or watch the network tab for /g/collect hits) and confirm each event fires once with the right params.
  • PostHog: watch the Activity / live events feed; confirm distinct_id is stable across the session.
  • Do not fire events on render. A capture() in a React component body or an unguarded useEffect re-fires on every re-render and double-counts. Fire on the user action, or in a useEffect with a correct dependency array / a fire-once guard.
  • Stitching: GA4 Measurement Protocol events must arrive within 48h of the client-side timestamp to stitch to the right client_id. If you set user_id server-side, set the same user_id browser-side or you create duplicate users.
  • Checking a PostHog feature flag emits a $feature_flag_called event — expected, not a bug; budget for it.

Verify

Run scripts/verify.sh [path] (default: cwd). It is a read-only static lint, never a network call. It flags: PII-looking literals inside capture( / gtag('event' / .track( calls; GA4 event names that break the ≤ 40-char / leading-letter / charset rule; GA present without a gtag('consent','default' gate; and posthog.init( with no host (reverse-proxy reminder). It exits 0 on a clean or empty target.

Anti-patterns

Anti-patternWhy it bitesDo instead
Rename a live event in prodForks the metric; old funnel flatlines, new one starts at zeroAdd a new event; deprecate the old one in a doc, never rename
Treat autocapture as the taxonomyAutocapture is noisy DOM events, not your domain — funnels become unbuildableDesign named domain events; autocapture is a supplement
Email/token in event propsTurns analytics into a breach-reportable PII store; violates the DPAScrub at a capture() wrapper; ids only
No consent gate for EEA/UKSince 21 Jul 2025, Google drops conversions/remarketing/demographicsgtag('consent','default', denied) before config; PostHog optOut
capture() in render / unguarded effectRe-fires every re-render → double-countingFire on the action or a fire-once-guarded effect
Server user_id ≠ browser user_idCreates duplicate users; funnel splitsUse the same stable id on both sides; stitch within 48h
posthog.init with no proxy hostAd-blockers eat ~20-40% of eventsServe SDK + ingest under a first-party path (/ingest)
Email as GA4 user_idPII leak + Google policy violationA stable opaque id (DB id / UUID)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analytics AI skill do?

Use when instrumenting product or web analytics — GA4/PostHog SDK wiring, event taxonomy, funnels, double-counted events, consent gating, PII scrubbing. NOT charting that data (that is dashboard), NOT choosing which metrics matter (that is kpi-framework), NOT experiment math (that is ab-testing), NOT cookie-policy text (that is gdpr-privacy).

Why use Analytics on TypingMind?

Because you install it once and use it with any model. Analytics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analytics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/analytics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analytics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analytics?

As many as you like. As long as a model supports skills, you can use Analytics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analytics AI skill free?

Yes. It is published on GitHub by ericrisco under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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