CCG is a command-line workflow engine that coordinates Claude, Codex, Gemini, and other models as specialized collaborators on coding tasks. It is used to analyze requests, choose a strategy, delegate work to model-specific roles, and combine their results. The catalogue entries provide the skills, commands, agents, and plugin that implement this workflow.
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 fengshao1227/ccg-workflow --skill data-engineeringgit clone --depth 1 https://github.com/fengshao1227/ccg-workflowWrote 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/fengshao1227/ccg-workflow/data-engineering)<a href="https://agentmods.dev/skills/fengshao1227/ccg-workflow/data-engineering"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/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/fengshao1227/ccg-workflow/data-engineering"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/data-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- data-engineering — 100% identical, 414 lines differ
- data-engineering — 100% identical, 0 lines differ
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 fengshao1227/ccg-workflow (5,945 stars, last pushed 5d 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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