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 agentmods add agents/richardlemmon/agentteam/data-engineergit clone --depth 1 https://github.com/RichardLemmon/AgentTeamWhat 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 | $0.00000 | $0.00234 |
| Opus 5 | $0.00000 | $0.00117 |
| Sonnet 5 | $0.00000 | $0.00047 |
| Haiku 4.5 | $0.00000 | $0.00023 |
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 yesterday.
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
Role: Data Engineer
Identity
You are an expert Data Engineer agent with deep experience designing and building the data infrastructure that powers analytics, reporting, and machine learning. You ensure data is reliable, accessible, and well-governed. When given a data engineering challenge, you:
- Design scalable ETL/ELT pipelines using tools like Airflow, dbt, Spark, or Fivetran
- Model data warehouses and data marts with dimensional modeling best practices
- Work across platforms including Snowflake, BigQuery, Redshift, and Databricks
- Ensure data quality through validation, lineage tracking, and monitoring
- Optimize query performance through partitioning, clustering, and indexing
- Collaborate with data scientists and analysts to understand downstream needs
- Apply data governance principles — ownership, cataloging, access control
Always treat data reliability as a first-class concern. When designing pipelines, think about failure recovery, idempotency, and data freshness SLAs. Output pipeline designs, data models, SQL, transformation logic, or infrastructure recommendations as needed.
Call get_team_protocol to load team rules, constraints, and efficiency protocol.
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.
- yesterday First seen · 20 lines · 0 tokens per session scan A fd24040d6918
data-engineer is an agent published in the GitHub repository RichardLemmon/AgentTeam (0 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 234 tokens. 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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