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 organvm-iv-taxis/a-i--skills --skill data-pipeline-architectgit clone --depth 1 https://github.com/organvm-iv-taxis/a-i--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/organvm-iv-taxis/a-i--skills/data-pipeline-architect)<a href="https://agentmods.dev/skills/organvm-iv-taxis/a-i--skills/data-pipeline-architect"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/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/organvm-iv-taxis/a-i--skills/data-pipeline-architect"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/data-pipeline-architect.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 | $0.00037 | $0.01330 |
| Opus 5 | $0.00018 | $0.00665 |
| Sonnet 5 | $0.00007 | $0.00266 |
| Haiku 4.5 | $0.00004 | $0.00133 |
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 13d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Architect
This skill provides guidance for designing robust, scalable data pipelines that move data reliably from sources to destinations.
Core Competencies
- ETL vs ELT: Traditional Extract-Transform-Load vs modern Extract-Load-Transform patterns
- Orchestration: Airflow, Dagster, Prefect, dbt for workflow management
- Data Quality: Validation, monitoring, lineage tracking
- Scalability: Batch vs streaming, partitioning, parallelization
Pipeline Design Process
1. Requirements Analysis
To begin pipeline design, gather:
- Source systems and data formats (APIs, databases, files, streams)
- Target destinations (data warehouse, lake, lakehouse)
- Freshness requirements (real-time, hourly, daily)
- Data volume and velocity estimates
- Quality and compliance requirements
2. Architecture Selection
Batch Pipelines - For periodic bulk processing:
- Schedule-driven (hourly, daily, weekly)
- Higher latency tolerance
- Simpler error recovery (re-run entire batch)
- Tools: Airflow, dbt, Spark
Streaming Pipelines - For real-time requirements:
- Event-driven processing
- Sub-second to minute latency
- Complex state management
- Tools: Kafka, Flink, Spark Streaming
Hybrid Approaches - Lambda or Kappa architecture:
- Batch layer for completeness
- Speed layer for low latency
- Serving layer for queries
3. ETL vs ELT Decision
ETL (Transform before Load):
- When target has limited compute
- When transformation reduces data volume significantly
- When sensitive data must be masked before landing
- Legacy data warehouse patterns
ELT (Transform after Load):
- Modern cloud warehouses with cheap compute
- When raw data preservation is needed
- When transformations change frequently
- dbt-style transformations in warehouse
4. Pipeline Components
Extraction Layer:
- Full extraction vs incremental (CDC, timestamp-based)
- API pagination and rate limiting
- Connection pooling and retry logic
- Schema detection and drift handling
What ships with it
4 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.
- 13d ago First seen · 175 lines · 37 tokens per session scan A 918593e4b876
data-pipeline-architect is a skill published in the GitHub repository organvm-iv-taxis/a-i--skills (17 stars, last pushed 16d ago), licensed Apache-2.0. It adds 37 tokens to every session and 1,330 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 2026-08-30.
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