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 OneWave-AI/claude-skills --skill data-pipeline-buildergit clone --depth 1 https://github.com/OneWave-AI/claude-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/onewave-ai/claude-skills/data-pipeline-builder)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/data-pipeline-builder"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/data-pipeline-builder/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/onewave-ai/claude-skills/data-pipeline-builder"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/data-pipeline-builder.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 | $0.00058 | $0.00800 |
| Opus 5 | $0.00029 | $0.00400 |
| Sonnet 5 | $0.00012 | $0.00160 |
| Haiku 4.5 | $0.00006 | $0.00080 |
Grade A, and why
data-pipeline-builder 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 9d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Builder
Design and implement production-grade ETL/ELT data pipelines: take data sources, a destination, and transformation requirements, then produce a complete pipeline specification plus all implementation files needed to run it.
Contents
references/project-structure.md-- output file layout, architecture pattern selection, component selection.references/python-patterns.md-- Python code standards and base extractor/transformer/loader/retry patterns.references/quality-checks.md-- composable data quality check framework and built-in checks.references/orchestration-config.md-- Airflow DAG, pipeline config YAML, and monitoring/alerting patterns.references/spec-template.md-- thedata-pipeline-spec.mdoutput template.
Workflow
-
Gather requirements. If the user gave clear requirements, proceed to design. Otherwise ask targeted questions: data sources (databases, APIs, files, streams); destination (warehouse, lake, database); transformations (joins, aggregations, filters, business rules); freshness requirement (real-time, hourly, daily); technology preferences (Airflow, dbt, Spark, cloud provider); data quality and compliance requirements.
-
Analyze and design. Catalog each source (connection type, auth, schema, volume, CDC availability, rate limits). Define the destination (platform, schema design, partitioning, clustering, access patterns). Map transformations (field mappings, business logic, type conversions, joins, aggregations, deduplication, SCD handling, derived fields). Establish non-functional requirements (freshness SLA, processing window, failure tolerance, retention, compliance). Select an architecture pattern and components per
references/project-structure.md. -
Present the design before generating code. Confirm architecture, sources, destination, schedule, key transformations, and quality gates with the user, then proceed on approval.
-
Generate implementation. Produce all files following the layout in
references/project-structure.md, customized to the specific pipeline with no placeholder code requiring manual editing:- For each source, generate a concrete extractor inheriting from
BaseExtractor(seereferences/python-patterns.md). - For each transformation, generate a concrete transformer class or SQL file.
- For each destination, generate a concrete loader inheriting from
BaseLoader. - Generate the Airflow DAG with all task dependencies wired up and the pipeline config YAML (see
references/orchestration-config.md). - Generate quality checks tailored to the data and monitoring config with appropriate alert thresholds (see
references/quality-checks.mdandreferences/orchestration-config.md). - Generate tests for all custom business logic.
- For each source, generate a concrete extractor inheriting from
What ships with it
5 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.
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.
- 9d ago First seen · 49 lines · 58 tokens per session scan A ab9445631a77
data-pipeline-builder is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 800 once invoked, about $0.0003 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 2026-09-03.
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