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 JayRHa/AgentSkills --skill data-pipeline-architectgit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/jayrha/agentskills/data-pipeline-architect)<a href="https://agentmods.dev/skills/jayrha/agentskills/data-pipeline-architect"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/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/jayrha/agentskills/data-pipeline-architect"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/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 | $0.00132 | $0.01816 |
| Opus 5.5 | $0.00053 | $0.00726 |
| Sonnet 5.5 | $0.00026 | $0.00363 |
| Haiku 4.5 | $0.00013 | $0.00182 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Architect
Overview
This skill helps you design and review production-grade data pipelines. It covers the full lifecycle: ingestion, transformation, idempotency and backfills, schema evolution, orchestration, and data quality. It is opinionated toward modern ELT (load raw, transform in-warehouse) but supports classic ETL where it fits.
Keywords: ETL, ELT, data pipeline, ingestion, idempotency, backfill, schema evolution, schema drift, CDC, incremental load, watermark, orchestration, Airflow, Dagster, Prefect, dbt, data quality, freshness, dedup, late-arriving data, partitioning, medallion, bronze silver gold.
Use this skill to produce a concrete pipeline design (a design doc), to review an existing pipeline against best practices, or to generate skeleton DAGs/models and data-quality checks.
Decision: ETL vs ELT
Default to ELT when the destination is a modern columnar warehouse/lake (Snowflake, BigQuery, Redshift, Databricks, DuckDB). Land raw data first, transform with SQL/dbt. Use ETL when: the destination can't transform cheaply, you must mask/drop PII before it lands (compliance), or you transform in-flight for a stream. See references/etl-vs-elt.md.
Workflow
Follow these steps in order. Produce the design document in templates/pipeline-design.md as you go.
-
Clarify requirements. Capture: sources, destination, SLA/freshness (real-time, hourly, daily), volume (rows/day, GB/day), data sensitivity (PII?), and consumers (BI, ML, reverse-ETL). Don't design before you know freshness and volume — they drive batch-vs-stream and incremental-vs-full.
-
Choose load pattern. Decide ETL vs ELT (above) and batch vs streaming. Map each source to an extraction strategy: full snapshot, incremental by watermark, or CDC. See
references/ingestion-patterns.md. -
Design for idempotency. Every load step must be safe to re-run and produce the same result. Use the techniques in
references/idempotency.md: deterministic partition keys, MERGE/upsert on a stable business key, delete-insert by partition, or staging-then-atomic-swap. Never blindINSERTinto a target on retry.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/orders-pipeline.md 3.2 KB
- references/data-quality.md 3.3 KB
- references/etl-vs-elt.md 2.4 KB
- references/idempotency.md 2.9 KB
- references/ingestion-patterns.md 3.2 KB
- references/orchestration.md 3.1 KB
- references/schema-evolution.md 3.0 KB
- scripts/pipeline_lint.py 8.6 KB runs code
- templates/pipeline-design.md 2.2 KB
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 · 87 lines · 132 tokens per session scan A fdabba9474dc
data-pipeline-architect is a skill published in the GitHub repository JayRHa/AgentSkills (12 stars, last pushed 2mo ago), licensed MIT. It adds 132 tokens to every session and 1,816 once invoked, about $0.0005 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 2026-08-31.
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git-commit-writer
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