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 finsilabs/awesome-ecommerce-skills --skill ecommerce-data-warehousegit clone --depth 1 https://github.com/finsilabs/awesome-ecommerce-skillsWrote 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/finsilabs/awesome-ecommerce-skills/ecommerce-data-warehouse)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/ecommerce-data-warehouse"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/ecommerce-data-warehouse/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/finsilabs/awesome-ecommerce-skills/ecommerce-data-warehouse"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/ecommerce-data-warehouse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00035 | $0.02609 |
| Opus 5 | $0.00017 | $0.01305 |
| Sonnet 5 | $0.00007 | $0.00522 |
| Haiku 4.5 | $0.00003 | $0.00261 |
Grade A, and why
ecommerce-data-warehouse 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
E-commerce Data Warehouse
Overview
An ecommerce data warehouse centralizes data from your store, ad platforms, shipping carriers, and accounting system into a single analytics layer — enabling reports that no single platform can produce on its own. Most merchants start needing a warehouse when they outgrow their platform's built-in analytics (typically around $1M+ ARR or when managing multiple channels), want to combine ad spend with order data for true ROAS, or need SQL-level access to run custom cohort and attribution analyses.
This skill guides you through extracting data from your platform, loading it into BigQuery (recommended for cost), and transforming it with dbt so your team can build dashboards in Looker Studio, Metabase, or Tableau.
When to Use This Skill
- When your platform's built-in analytics cannot answer important business questions
- When you need to combine data from multiple sources (Shopify + Meta Ads + Google Ads + shipping)
- When you want to build custom cohort retention, LTV, and attribution models
- When analysts or data scientists need SQL access to raw order data
- When you need a single source of truth across multiple sales channels
Core Instructions
Step 1: Choose your warehouse and set up the infrastructure
For most ecommerce merchants, BigQuery is the best starting point:
- Free tier: first 10 GB storage + 1 TB queries per month free
- No infrastructure to manage — serverless
- Excellent integrations with Looker Studio (free BI tool), dbt, and every major data integration tool
Alternatives:
- Snowflake: Better for teams that need time-travel, data sharing, or advanced concurrency; more expensive at small scale
- Redshift: Good if you are already in AWS; more operational overhead than BigQuery or Snowflake
Set up BigQuery:
- Go to console.cloud.google.com and create a new Google Cloud project
- Enable the BigQuery API
- Create a dataset (e.g.,
ecommerce_raw) for raw ingested data and a second dataset (ecommerce_analytics) for transformed tables
What ships with it
7 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.
- evals/customer-ltv-segmentation-and-kpi-calcul/criteria.json 2.8 KB
- evals/customer-ltv-segmentation-and-kpi-calcul/task.md 2.2 KB
- evals/dbt-project-layering-and-transformation-/criteria.json 2.9 KB
- evals/dbt-project-layering-and-transformation-/task.md 3.6 KB
- evals/star-schema-dimensional-model-design/criteria.json 2.9 KB
- evals/star-schema-dimensional-model-design/task.md 1.7 KB
- tile.json 247 B
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.
- 12d ago First seen · 243 lines · 35 tokens per session scan A f26bbf56a41d
ecommerce-data-warehouse is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 35 tokens to every session and 2,609 once invoked, about $0.0002 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 2026-08-30.
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