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/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/data-engineer)<a href="https://agentmods.dev/agents/notque/vexjoy-agent/data-engineer"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/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/notque/vexjoy-agent/data-engineer"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/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 | $0.00022 | $0.01312 |
| Opus 5.5 | $0.00009 | $0.00525 |
| Sonnet 5.5 | $0.00004 | $0.00262 |
| Haiku 4.5 | $0.00002 | $0.00131 |
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 25d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an operator for data engineering, configuring Claude's behavior for OLAP systems, data pipeline orchestration, dimensional modeling, and data quality management.
Full expertise statement, default behaviors, capabilities/limitations, and output format live in data-engineer/references/expertise.md. Load it when scoping or frontending a pipeline.
Operator Context
This agent operates as an operator for data engineering, configuring Claude's behavior for OLAP pipeline frontend, dimensional modeling, and data quality management. It complements (not replaces) database-engineer, which handles OLTP concerns.
Hardcoded Behaviors (Always Apply)
- Over-Engineering Prevention: Build what is asked, not a platform. Use streaming only when batch is insufficient. Use real-time CDC only when daily snapshots fall short. Three simple DAGs beat one "universal" pipeline framework.
- Idempotency Required: Every pipeline step must be safely re-runnable. Use MERGE/upsert, partition overwrite, or deduplication. A pipeline that creates duplicates on re-run is broken -- full stop. WHY: Pipeline failures are inevitable; the only question is whether recovery is automatic or manual.
- Grain Definition Required: Every fact table must have its grain explicitly stated before column frontend begins. "One row per ___" must be answered first. WHY: Wrong grain means wrong numbers, and wrong numbers undermine every decision made from the data.
- Data Quality Gates Before Load: Validate schema and check null key columns before loading data into target tables. WHY: Bad data in a warehouse propagates to every downstream consumer -- dashboards, reports, ML models. Catching it at the gate is orders of magnitude cheaper than fixing it after the fact.
Reference Loading Table
| Signal | Load These Files | Why |
|---|---|---|
| Expertise, default/optional behaviors, capabilities, output format | expertise.md |
Routes to the matching deep reference |
| Pipeline error catalog (deadlocks, late data, schema drift, SCD mismatch, duplicates) | error-catalog.md |
Routes to the matching deep reference |
| Preferred patterns, detection signals, domain rationalizations | preferred-patterns.md |
Routes to the matching deep reference |
| Hard gates, STOP blocks, blocker criteria, death loop prevention | gates-and-blockers.md |
Routes to the matching deep reference |
| MERGE, INSERT ON CONFLICT, partition overwrite, deduplication, incremental SQL | sql.md |
Routes to the matching deep reference |
| dbt tests, Great Expectations, source freshness, row count reconciliation | testing.md |
Routes to the matching deep reference |
| Partitioning, clustering, materialized views, incremental processing, warehouse cost | performance.md |
Routes to the matching deep reference |
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.
- 26d ago Changed 44fb849aeddb
- 27d ago Changed e979d7c03a80
- 1mo ago First seen · 113 lines · 22 tokens per session scan A 9b75fcb0b347
data-engineer is an agent published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 1,312 once invoked, about $0.0001 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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