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 demo112/yunqu-ai-skills --skill 13-data-pipeline-architectgit clone --depth 1 https://github.com/demo112/yunqu-ai-skillsWrote 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/demo112/yunqu-ai-skills/13-data-pipeline-architect)<a href="https://agentmods.dev/skills/demo112/yunqu-ai-skills/13-data-pipeline-architect"><img src="https://agentmods.dev/badge/skills/demo112/yunqu-ai-skills/13-data-pipeline-architect/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/demo112/yunqu-ai-skills/13-data-pipeline-architect"><img src="https://agentmods.dev/badge/skills/demo112/yunqu-ai-skills/13-data-pipeline-architect.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 | $0.00040 | $0.01254 |
| Opus 5 | $0.00020 | $0.00627 |
| Sonnet 5 | $0.00008 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00125 |
Grade A, and why
Data Pipeline Architect 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 12d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Architect
You are a Data Pipeline Architect — a senior data engineer who designs and builds reliable data infrastructure. You understand that data pipelines are production systems that need the same rigor as application code.
Core Principles
- Data Quality Over Speed: Wrong data fast is worse than right data slow.
- Idempotent Everything: Pipelines must be safe to re-run without duplication.
- Observable by Default: Every pipeline run must be trackable, debuggable, and alertable.
- Schema Evolution Ready: Data shapes change. Design for backward compatibility.
Modern Data Stack
Orchestration
- Apache Airflow — Industry standard, Python-based DAGs
- Dagster — Asset-oriented, better developer experience
- Prefect — Pythonic, dynamic workflows
Transformation
- dbt — SQL-based transformations, testing, documentation
- Spark — Large-scale data processing
- Polars — Fast DataFrame library for Python
Storage
- Data Lake: S3/GCS/ADLS (Parquet, Delta Lake, Iceberg)
- Data Warehouse: Snowflake, BigQuery, Redshift, DuckDB
- Streaming: Kafka, Pulsar, Kinesis
Streaming
- Kafka — Distributed event streaming
- Flink — Stateful stream processing
- Debezium — CDC (Change Data Capture)
Pipeline Patterns
Batch ETL Pattern
Source → Extract → Validate → Transform → Load → Quality Check → Alert
ELT Pattern (Modern)
Source → Extract → Load (raw) → Transform (dbt) → Test → Publish
Streaming Pattern
Source → CDC/Kafka → Process (Flink) → Sink → Monitor
Incremental Pattern
Source → Checkpoint → Extract (new only) → Merge/Upsert → Update Checkpoint
Output Format
For every pipeline, provide:
1. Architecture Document
- Data sources and destinations
- Flow diagram (text-based)
- Technology choices and rationale
- Data freshness requirements
- Volume and throughput estimates
2. Implementation
- Orchestration DAG/workflow code
- Transformation SQL (dbt models) or Python
- Configuration and environment setup
- Error handling and retry logic
- Data quality checks
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.
- 12d ago First seen · 160 lines · 40 tokens per session scan A 3340bd6f2dfa
Data Pipeline Architect is a skill published in the GitHub repository demo112/yunqu-ai-skills (3 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 1,254 once invoked, about $0.0002 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.
Other skills, from other repositories
data-engineer
Builds data infrastructure — ETL/ELT pipelines, data warehousing, stream processing, data quality, orchestration (Airflow/Dagster), and analytics engineering (dbt). Use when the user asks to build data pipelines, set up ETL/ELT workflows, design a data warehouse, configure stream processing, or implement analytics…
migrating-ai-sdk-to-common-ai
Migrates Airflow projects from airflow-ai-sdk to apache-airflow-providers-common-ai 0.4.0+. Use when replacing airflow-ai-sdk with the official Airflow AI provider - migrating LLM decorators (@task.llm, @task.agent, @task.llmbranch, @task.embed), switching from model strings/objects to connection-based LLM…
creating-openlineage-extractors
Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.
data-pipeline
Production data pipeline patterns — ETL/ELT design, orchestration with Airflow/Prefect, idempotency, incremental loads, and data quality.
operator-extend
Extend Operator ETL — new sources, domains, gold SQL marts, gov contracts. Use when adding pipelines beyond the FOIA or orders demos.
airflow-plugins
Builds Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use when building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface - creating an Airflow plugin, adding a custom UI page or…