modeling-dimension-tables

The small lookup lists every report leans on — country to region, plan to price tier, a calendar of dates — built once and cleanly, so each new report can join to them instead of rebuilding its own copy.

WHEN TO USE

SCOPE

USE FOR

Upload a country-to-region or plan-to-tier list and turn it into a table reports can join to.
Derive a list of countries or plans from the events your product already sends.
Attach a lookup to people or to a data table so its columns show up as ordinary filters.
Check a lookup has exactly one row per entry, so joining it never silently multiplies the results.
Generate a calendar-of-dates table and tested lookup models in dbt.

NOT FOR

Building an exchange-rate table in PostHog — its built-in currency conversion already covers that.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (7)

Modeling dimension tables (star schema)

Dimensions are the descriptive tables (dim_country, dim_plan, dim_date) that fact tables join to for slicing. This skill builds them once, cleanly, so every other model reuses them instead of re-deriving lookups. Read modeling-warehouse-foundations first (joins + convertCurrency() live there). Catalog of common dimensions: references/dimension-catalog.md; recipes in references/posthog/ and references/dbt/.

Star schema in one screen

Facts (events, charges, revenue items) are long, keyed, and additive. Dimensions are short, one row per entity, descriptive. You model a dimension in three moves:

Source it — where does the dimension data come from?
- Upload / seed a lookup (country→region, plan→tier) as a CSV (warehouse source or dbt seed).
- Sync it from a system of record (your app DB, Stripe products) as a warehouse source.
- Derive it from events (distinct countries seen, a plan property observed per person).
Shape it — an aliased SELECT with clean column names, one row per entity (dedupe hard). Save as a view; materialize it on a slow sync_frequency (7day/30day) since dimensions change rarely and are read constantly.
Attach it — a saved join (dimension → a fact table) or person join (dimension → persons) so its columns appear as native fields in any query, filter, or breakdown. See foundations joins-and-dimensions.md.

Currency is already a managed dimension — don't build it

PostHog ships exchange rates behind convertCurrency(from, to, amount, timestamp?) (Open Exchange Rates, historical-rate-correct). Use it directly for any money conversion. Only build a currency dimension yourself in dbt (which has no equivalent), or if you need a rate provider PostHog doesn't offer.

Rules before you model

One row per entity, unique key. A dimension with duplicate keys silently fan-outs every fact it joins. Test uniqueness (PostHog: verify in the shaping query; dbt: unique + not_null).
Alias to clean, stable names — country_code, region, plan_tier. These names become the join surface everything else depends on.
Materialize static dimensions on a slow schedule; don't leave a constantly-read lookup virtual.
Register and certify. Annotate the dimension and, if it's load-bearing, certify it in the catalog (foundations governance.md) so other models discover it and don't build a rival copy.
Prefer built-in currency (convertCurrency) over a hand-rolled FX table on PostHog.

Build it

PostHog: shape an aliased dimension view, then materialize + join. Recipes: references/posthog/dim_country.sql (derive + enrich from events), dim_plan.sql (lookup/upload pattern).

dbt: conformed dim_* models with unique/not_null/relationships tests, plus a generated dim_date. Recipes: references/dbt/.

File map

File	Read when
`references/dimension-catalog.md`	Common dimensions, how to source each, and the natural key.
`references/posthog/`	HogQL aliased-dimension view recipes.
`references/dbt/`	dbt `dim_date` / `dim_country` + `schema.yml` tests.

Companions

modeling-warehouse-foundations (joins + currency), setting-up-a-data-warehouse-source / suggesting-data-imports (sync/upload the source data), and the models that consume these dimensions: modeling-revenue-metrics, modeling-conversion-metrics, modeling-activation-metrics, modeling-product-usage-metrics.

This skill ships inside the PostHog AI Plugin. Listed package contents include 3 slash commands, 1 agent, hooks and an MCP server. Installed components and skill versions may vary by AI tool.

Install the whole plugin

This route installs the posthog plugin package selected for your AI tool.

Claude Code

Add PostHog's marketplace, then install the plugin

Run both lines in a terminal. They install PostHog's posthog plugin: this skill, PostHog's other skills, and a connection to PostHog's hosted MCP server.

claude plugin marketplace add PostHog/ai-plugin

02
```
claude plugin install posthog@posthog
```

Installation and loading

The plugin installs for you in every project and loads the next time you start Claude Code, or after /reload-plugins in an open session.
To connect your PostHog account, start claude, type /mcp, select plugin:posthog:posthog and follow the browser prompts.

Good to know

The plugin includes PostHog's session-capture hook; PostHog's README says it sends sessions to LLM Analytics only when POSTHOG_LLMA_CC_ENABLED=true and POSTHOG_API_KEY are both set.
If you use the separate posthog-telemetry plugin, PostHog says to keep only one of the two, because both register the same hook.
On a self-hosted PostHog, this route's MCP connection won't reach your instance: the plugin's config is fixed to https://mcp.posthog.com/mcp, which routes to PostHog's US or EU region. POSTHOG_MCP_URL works only for the Gemini CLI extension.

Install without the plugin (incomplete)

What's missing

Installing just this skill adds only its own folder. Its instructions also call for other plugin components, such as shared scripts, reference files or other skills, which aren't included. Installing the whole plugin avoids this gap.

Run this outside your AI tool, using Node.js 22.20.0 or newer. Skills CLI 1.7.2 skips skill selection; prompts cancel installation without an interactive terminal. If choosing Project when asked about installation scope, open your project's folder before running this.

Install checklist

Check selected tools if a picker appears; Claude Code may be unchecked under Additional agents. ↑/↓ moves, Space toggles selections, Enter confirms.
Without a tool picker, check Installing to: for your intended tool. Stop if it names a different tool.
If asked, choose Symlink (Recommended) for shared files or Copy to all agents for separate copies; review the summary before confirming installation.
Install the find-skills skill? defaults to Yes, adding another skill for all projects. Choose No if you don't want that extra installation.

Skills CLI 1.7.2 sends install data to skills.sh by default, including the source, skill names, target tools and file details; GitHub sources are reported only when GitHub confirms the repository is public, while other remote sources can also be reported. Confirmed-public GitHub sources also trigger a security-audit request. Before installing, set the environment variable DO_NOT_TRACK=1 or DISABLE_TELEMETRY=1 to disable both.

npx skills add posthog/ai-plugin --skill "modeling-dimension-tables" --full-depth

After installing for Claude Code, open it in the installation project if you chose Project; Global makes the skill available across projects on this computer. Once Claude Code loads the skill, its description helps Claude decide when to use it.

If installation created a top-level skills folder that did not exist when your Claude Code session started, run /reload-skills in that session. Run it again after later changes in that folder.

View on GitHub

Same repository

adding-warehouse-person-properties
Sync columns from a synced data warehouse table onto PostHog person or group properties, so warehouse data becomes usable anywhere person and group properties already work: feature flag targeting, cohorts, insight filters and breakdowns, surveys, session replay filters, workflows, and the person profile. Use when the user wants to "add a person property from my warehouse", "enrich people with Stripe/Postgres/Salesforce data", "put ARR or plan tier on my persons", "target a feature flag by a warehouse column", "sync warehouse columns onto groups or organizations", or wants to inspect, backfill, disable, or debug an existing warehouse-backed person or group property.Read full description
analyzing-expensive-users
Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.Read full description
analyzing-experiment-session-replays
Analyze session replay patterns across experiment variants to understand user behavior differences. Use when the user wants to see how users interact with different experiment variants, identify usability issues, compare behavior patterns between control and test groups, or get qualitative insights to complement quantitative experiment results. Also covers pairing the observed behavior with a linked survey when the user wants qualitative feedback beyond what recordings show.Read full description
assessing-heatmaps
Assesses what a page's heatmap is telling you and recommends concrete changes. Pulls click / rageclick / scroll-depth data for a URL, names the hot elements by cross-referencing autocapture events on the same page, and can create a saved heatmap the user opens in PostHog, then summarizes the behavior and proposes improvements. TRIGGER when: user asks what a heatmap shows, why people aren't clicking something, where users rage-click, how far they scroll, what to change on a page based on heatmap/click data, or to 'analyze/assess/review the heatmap' for a URL. DO NOT TRIGGER when: the user only wants to create a saved heatmap screenshot with no analysis (use heatmaps-saved-create directly), or is asking about session replay in general (use investigating-replay).Read full description
auditing-endpoints
Audit every endpoint in a PostHog project for staleness, failed materialisations, and unused materialised versions. Use when the user asks "what endpoints can I clean up?", "are any of my endpoints broken?", "which materialised versions are still being called?", or wants a one-shot cleanup pass over the Endpoints product. Produces a prioritised report grouped by issue type, with recommended actions but does not modify anything without explicit confirmation.Read full description
auditing-experiments-flags
Audit PostHog experiments and feature flags for configuration issues, staleness, and best-practice violations. Read when the user asks to audit, health-check, or review experiments or feature flags, check flag hygiene, or verify experiment setup.Read full description

Modeling dimension tables (star schema)

Star schema in one screen

Currency is already a managed dimension — don't build it

Rules before you model

Build it

File map

Companions

MCP connection required

Install the whole plugin

Add PostHog's marketplace, then install the plugin

More skills

adding-warehouse-person-properties

analyzing-expensive-users

analyzing-experiment-session-replays

assessing-heatmaps

auditing-endpoints

auditing-experiments-flags