Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PostHog/posthog-foss --skill modeling-dimension-tablesgit clone --depth 1 https://github.com/PostHog/posthog-fossWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/posthog/posthog-foss/modeling-dimension-tables)<a href="https://agentmods.dev/skills/posthog/posthog-foss/modeling-dimension-tables"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/modeling-dimension-tables/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/posthog/posthog-foss/modeling-dimension-tables"><img src="https://agentmods.dev/badge/skills/posthog/posthog-foss/modeling-dimension-tables.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00213 | $0.01198 |
| Opus 5 | $0.00106 | $0.00599 |
| Sonnet 5 | $0.00043 | $0.00240 |
| Haiku 4.5 | $0.00021 | $0.00120 |
Grade A, and why
modeling-dimension-tables scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- modeling-dimension-tables — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
SELECTwith clean column names, one row per entity (dedupe hard). Save as a view; materialize it on a slowsync_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.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 80 lines · 213 tokens per session scan A dae4a7565330
modeling-dimension-tables is a skill published in the GitHub repository PostHog/posthog-foss (715 stars, last pushed today), licensed MIT. It adds 213 tokens to every session and 1,198 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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