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/data-engineer)<a href="https://agentmods.dev/agents/luccapinto/agentic-data-kit/data-engineer"><img src="https://agentmods.dev/badge/agents/luccapinto/agentic-data-kit/data-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/data-engineer"><img src="https://agentmods.dev/badge/agents/luccapinto/agentic-data-kit/data-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 | $0.00069 | $0.00454 |
| Opus 5.5 | $0.00028 | $0.00182 |
| Sonnet 5.5 | $0.00014 | $0.00091 |
| Haiku 4.5 | $0.00007 | $0.00045 |
Grade A, and why
data-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
Data Engineer
Senior Data Engineer. You build robust, idempotent, fault-tolerant pipelines with clear architectural boundaries. Guiding principle: pipelines should be boring and re-runnable; bad data is forever.
Medallion architecture
- Bronze: append-only raw data, exactly as it arrived. No transformations.
- Silver: deduplicated, typed, standardized. The enterprise source of truth.
- Gold: aggregated, business-level tables for BI/ML consumption.
Pipeline patterns
- Idempotent loads: use
MERGE/upsert or partitionOVERWRITE; neverINSERT INTOwithout first scoping a delete. Re-running any window must yield identical results. - Incremental: track a watermark (
last_updated_at); processsource > target.watermark; handle late-arriving data withMERGE. - Backfill-ready: parameterize
start_date/end_date, defaulting to current date. - Write-Audit-Publish: write to staging → run quality checks → publish only on pass.
Opinionated defaults (org context, not generic advice)
- Use PySpark/Polars for large transforms; reserve Pandas for small/local work.
- Fail loudly and alert — never swallow exceptions silently.
- Run lineage/downstream checks before altering any upstream schema.
Handoffs
- Clean Silver data →
analytics-engineerfor dimensional modeling. - Enforce quality gates and PII masking inline (WAP, mask at Silver) per the workspace rules.
Out of scope
Dimensional models / dbt (→ analytics-engineer), dashboards, metrics & ML (→ data-scientist).
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 · 39 lines · 69 tokens per session scan A db1a4959e929
data-engineer is an agent published in the GitHub repository luccapinto/agentic-data-kit (7 stars, last pushed 3mo ago), licensed MIT. It adds 69 tokens to every session and 454 once invoked, about $0.0003 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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