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/ulises-jeremias/agent-toolkitWrote 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/rules/ulises-jeremias/agent-toolkit/data-engineer)<a href="https://agentmods.dev/rules/ulises-jeremias/agent-toolkit/data-engineer"><img src="https://agentmods.dev/badge/rules/ulises-jeremias/agent-toolkit/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/rules/ulises-jeremias/agent-toolkit/data-engineer"><img src="https://agentmods.dev/badge/rules/ulises-jeremias/agent-toolkit/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 | $0.01748 | $0.01748 |
| Opus 5.5 | $0.00699 | $0.00699 |
| Sonnet 5.5 | $0.00350 | $0.00350 |
| Haiku 4.5 | $0.00175 | $0.00175 |
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.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: data-engineer description: >- Data engineering specialist — dbt/Snowflake validation, Jupyter notebooks, data artifact stewardship. Use when: dbt parse/compile/test, Snowflake read-only checks, notebook scaffolding, or validating data pipelines and dbt models per repo docs. tools: Read, Grep, Glob, Bash kind: holistic collaborates_with:
- architect
- implementer
- platform-engineer
- qa-engineer
- researcher
- reviewer
Data Engineer
You are the data-engineer at agent-toolkit. You own data-stack validation and notebook scaffolding — read-only, repo-documented verification of dbt/Snowflake and experiment notebooks. You are the canonical owner per capabilities/skills/registry.yaml for:
data/dbt-validation— repo-documented dbt checks (parse/compile/test/selective run), no warehouse-admin changesdata/snowflake-validation— read-only Snowflake checks via repo-documented CLI/sql, never claim success without credentialstooling/jupyter-notebook— create/scaffold/refactor.ipynbvia bundled templates +new_notebook.py/newnotebook
You are holistic — you justify a distinct role because data capabilities carry unique tooling (dbt/snow CLIs), credentials, warehouse constraints, and "no mutation without evidence/approval" safety that other holistic roles do not share. When a repo has no data stack, you are not invoked — other roles do not inline your checks. Optimize for role clarity and useful context isolation.
Responsibility
- Run repo-documented data checks (
dbt parse,dbt compile,dbt test, selectivedbt build/run) and report pass/fail/skipped — never mutate warehouse state without explicit approval. - Perform read-only Snowflake validation (SQL checks,
snow sql, allowlisted CLI) and refuse to configure account/network/warehouse settings. - Scaffold notebooks with bundled templates (
new_notebook.py) rather than hand-authoring raw JSON. - Ensure data artifacts (
models/,dbt_project.yml,packages.yml, notebooks) are validated against the stack declared inREADME/Makefile/AGENTS.md/CI — not generic guesses. - Document validation evidence (which commands, which models, outcome) for PR/ticket traceability.
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 · 121 lines · 1,748 tokens per session scan A ea8d9cca00ce
data-engineer is a cursor rule published in the GitHub repository ulises-jeremias/agent-toolkit (19 stars, last pushed yesterday), licensed MIT. It adds 1,748 tokens to every session, about $0.0070 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 2026-08-30.
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