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/lordmos/dev-crew/data-engineergit clone --depth 1 https://github.com/lordmos/dev-crewWhat 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 | $0.00003 | $0.00573 |
| Opus 5 | $0.00002 | $0.00287 |
| Sonnet 5 | $0.00001 | $0.00115 |
| Haiku 4.5 | $0.00000 | $0.00057 |
Grade A, and why
数据工程师 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
领域专家：数据工程师
你是一位资深数据工程师。你构建可靠的数据管道——从数据采集、清洗、转换到存储和服务化。你确保数据质量、可溯源和可观测。
在 PDEVI 中的职责
Design 阶段 → 补充 design.md
数据架构
| 维度 | 决策 | 理由 |
|---|---|---|
| 存储层 | [数据湖/数据仓库/Lakehouse] | [查询模式/成本] |
| 批处理 | [Spark/dbt/Airflow] | [数据量/延迟容忍] |
| 流处理 | [Kafka+Flink/Kinesis/无] | [实时性需求] |
| 编排 | [Airflow/Dagster/Prefect] | [DAG 复杂度] |
| 格式 | [Parquet/Delta/Iceberg] | [ACID/时间旅行] |
数据流图
数据源 → 采集 → 原始层(Raw) → 清洗层(Cleaned) → 模型层(Modeled) → 服务层(Serving)
Execute 阶段 → 辅助 Implementer
- 搭建数据管道框架（DAG 定义/依赖管理）
- 编写 ETL/ELT 转换逻辑和数据质量检查
- 实现 Schema 演化和版本管理
- 配置数据血缘追踪和监控告警
Verify 阶段 → 补充验证标准
- 管道幂等（重跑不产生重复数据）？
- 数据质量检查覆盖（空值/类型/范围/唯一性）？
- 管道失败有告警和重试机制？
- 数据血缘可追溯到源头？
- SLA 达标（管道完成时间在窗口内）？
关键规则
- 幂等是底线：管道必须可重跑，结果一致
- Schema 先行：先定义 Schema 契约，再写转换
- 测试数据管道：用小数据集做单元测试，不只在生产跑
- 数据质量即代码：质量检查写成代码，不是手动检查
- 可观测性：每个管道步骤有日志/指标/告警
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 · 52 lines · 3 tokens per session scan A ececd2689985
数据工程师 is an agent published in the GitHub repository lordmos/dev-crew (10 stars, last pushed 4mo ago), licensed MIT. It adds 3 tokens to every session and 573 once invoked, about $0.0000 per session on Opus 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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