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 skills add qinye6/pi-ccg --skill data-engineeringgit clone --depth 1 https://github.com/qinye6/pi-ccgWrote 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/skills/qinye6/pi-ccg/data-engineering)<a href="https://agentmods.dev/skills/qinye6/pi-ccg/data-engineering"><img src="https://agentmods.dev/badge/skills/qinye6/pi-ccg/data-engineering/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/skills/qinye6/pi-ccg/data-engineering"><img src="https://agentmods.dev/badge/skills/qinye6/pi-ccg/data-engineering.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 | $0.00053 | $0.02391 |
| Opus 5.5 | $0.00021 | $0.00956 |
| Sonnet 5.5 | $0.00011 | $0.00478 |
| Haiku 4.5 | $0.00005 | $0.00239 |
Grade A, and why
data-engineering 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.
This is a copy
100% identical to data-engineering — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
数据工程域 · Data Engineering
域概览
数据工程域涵盖数据管道编排、流式处理、数据质量保障三大核心领域。
数据管道层 流处理层 质量保障层
├── Airflow (调度编排) ├── Kafka Streams ├── Great Expectations
├── Dagster (资产管理) ├── Flink ├── dbt
└── Prefect (现代工作流) └── Spark Streaming └── Soda Core
数据管道编排
框架对比
| 特性 | Airflow | Dagster | Prefect |
|---|---|---|---|
| 核心模型 | DAG + Task | Asset + Op | Flow + Task |
| 学习曲线 | 陡峭 | 中等 | 平缓 |
| 资产管理 | 无 | 原生支持 | 无 |
| 动态任务 | 支持 | 支持 | 支持 |
| 本地开发 | 复杂 | 简单 | 简单 |
| 社区生态 | 最大 | 成长中 | 成长中 |
Airflow 核心模式
- DAG 定义：
with DAG(dag_id, schedule, default_args) as dag - TaskFlow API：
@task装饰器，自动 XCom 传递 - 动态任务：
@task+.expand()实现 dynamic task mapping - Operators：PythonOperator / BashOperator / SQL / HTTP / S3
- Sensors：FileSensor / HttpSensor / ExternalTaskSensor
- 重试策略：
retries=3, retry_delay=timedelta(minutes=5), retry_exponential_backoff=True - 失败回调：
on_failure_callback发送告警 - SLA 监控：
sla=timedelta(hours=2)+sla_miss_callback
Dagster 核心模式
- Asset 定义：
@asset(group_name, deps)声明数据资产 - MaterializeResult：返回元数据（行数、预览等）
- Resources：
ConfigurableResource管理外部连接 - Jobs：
define_asset_job(selection=AssetSelection.groups(...)) - Schedules：
ScheduleDefinition(job, cron_schedule) - Sensors：
@sensor(job)监听外部事件触发 - Partitions：
DailyPartitionsDefinition按日分区 - Asset Checks：
@asset_check验证数据新鲜度/质量
Prefect 核心模式
- Flow/Task：
@flow+@task(retries=3, cache_key_fn=task_input_hash) - 并发：
ConcurrentTaskRunner+task.map(items) - Deployments：
Deployment.build_from_flow(schedule=CronSchedule(...)) - Blocks：
Secret/JSON管理配置和密钥
调度策略 Checklist
- Cron 表达式正确（
0 2 * * *日批 /*/15 * * * *实时） - 事件驱动：文件到达 / S3 / API 触发
- 跨 DAG 依赖：ExternalTaskSensor / Asset deps
- 幂等性：UPSERT / 分区覆盖写入
- 增量处理：
WHERE updated_at > last_run - 数据血缘：Dagster 原生 / Airflow Lineage / dbt ref()
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 · 208 lines · 53 tokens per session scan A 50ee1cac4a62
data-engineering is a skill published in the GitHub repository qinye6/pi-ccg (4 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 2,391 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to data-engineering, differing in 0 lines, and is treated as a copy.
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