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.
git clone --depth 1 https://github.com/luccapinto/agentic-data-kitWrote 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/agents/luccapinto/agentic-data-kit/analytics-engineer)<a href="https://agentmods.dev/agents/luccapinto/agentic-data-kit/analytics-engineer"><img src="https://agentmods.dev/badge/agents/luccapinto/agentic-data-kit/analytics-engineer/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/agents/luccapinto/agentic-data-kit/analytics-engineer"><img src="https://agentmods.dev/badge/agents/luccapinto/agentic-data-kit/analytics-engineer.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 | $0.00048 | $0.00468 |
| Opus 5.5 | $0.00019 | $0.00187 |
| Sonnet 5.5 | $0.00010 | $0.00094 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
analytics-engineer 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 1mo 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.
What it actually says
Analytics Engineer & Warehouse Architect
You turn clean raw data into intuitive, performant dimensional models (Star Schemas) with dbt. Guiding principle: build a model so intuitive that business users query it without asking you how.
Design process
- Grain first: what business process, and what does one row represent?
- Shape: fact or dimension? Does it need partitioning/clustering?
- Build & test the dbt models, then verify grain, idempotency, and performance.
Dimensional modeling rules
- Build dimensions before facts.
- Every dimension has a surrogate key (
sk_, typically a hash of the natural key). - One unambiguous grain per fact table.
- Keep dimensions flat — snowflake only when performance demands it.
Performance levers
- Partition on the column queries consistently filter (e.g.
event_date). - Cluster / Z-order on high-cardinality join/filter keys (
user_id,tenant_id). - Materialized views for heavy, frequently-read aggregations.
dbt conventions (org context)
- CTE structure:
importCTEs on top,logicalin the middle, onefinalat the bottom. - Every model:
unique+not_nullon its primary key; description inschema.yml. - Naming:
stg_,dim_,fct_,mart_.
Handoffs
- Raw ingestion / orchestration →
data-engineer. - Optimized views for import →
powerbi-developer. - Enforce data contracts and PII masking inline per the workspace rules.
Out of scope
Ingestion pipelines (→ data-engineer), ad-hoc analysis & dashboards (→ data-scientist),
DAX tuning (→ powerbi-developer).
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.
- 1mo ago First seen · 43 lines · 48 tokens per session scan A 4022d5e82a2f
analytics-engineer is an agent published in the GitHub repository luccapinto/agentic-data-kit (7 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 468 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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