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 mickeyyaya/refactoring-skills --skill data-pipeline-patternsgit clone --depth 1 https://github.com/mickeyyaya/refactoring-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/mickeyyaya/refactoring-skills/data-pipeline-patterns)<a href="https://agentmods.dev/skills/mickeyyaya/refactoring-skills/data-pipeline-patterns"><img src="https://agentmods.dev/badge/skills/mickeyyaya/refactoring-skills/data-pipeline-patterns/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/mickeyyaya/refactoring-skills/data-pipeline-patterns"><img src="https://agentmods.dev/badge/skills/mickeyyaya/refactoring-skills/data-pipeline-patterns.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 | $0.00080 | $0.04810 |
| Opus 5.5 | $0.00032 | $0.01924 |
| Sonnet 5.5 | $0.00016 | $0.00962 |
| Haiku 4.5 | $0.00008 | $0.00481 |
Grade A, and why
data-pipeline-patterns scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
user = requests.get(...) # inline enrichment How it starts
The opening of the file, as written. The whole thing — 531 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Patterns for Code Review
Overview
Pipelines fail silently, duplicate records, or stall under backpressure — often in production, at scale, and without clear error messages. Use this guide when reviewing data ingestion, transformation, or delivery code to catch structural hazards before they cause data loss or corruption.
When to use: Reviewing ETL/ELT jobs, stream processors, batch schedulers, orchestration DAGs, message consumers, or any code that moves data between systems at scale.
Quick Reference
| Pattern | Core Idea | Primary Red Flag |
|---|---|---|
| ETL vs ELT | Transform before or after load based on compute location | Transforming in application code when the warehouse can do it cheaper |
| Batch vs Streaming vs Micro-Batch | Select processing model to match latency and throughput requirements | Streaming when latency does not matter; polling when event triggers exist |
| Idempotency / Exactly-Once | Safe re-runs produce the same result | No idempotency key; INSERT without upsert guard; re-run creates duplicates |
| Watermarking / Checkpointing | Track progress so pipelines resume without reprocessing | No checkpoint state; always reprocessing from epoch on restart |
| Dead Letter Queue (DLQ) | Route unprocessable records for later inspection | Silently dropping failed records; no DLQ monitoring alert |
| Backpressure / Flow Control | Producer slows down when consumer cannot keep up | Unbounded in-memory queues; producer ignores consumer lag |
| Schema Evolution / Data Contracts | Backwards-compatible schema changes with explicit contracts | Breaking schema changes deployed without consumer coordination |
| Pipeline Orchestration / DAG | Explicit dependency graph with retries, SLAs, and alerting | Linear scripts with no dependency tracking, no retry, no alerting |
Patterns in Detail
1. ETL vs ELT Decision Framework
When to use ETL (Extract → Transform → Load):
- Sensitive data must be masked or anonymized before entering the warehouse
- Target system has no compute capacity (legacy database, constrained storage)
- Transformation logic is complex and not expressible in SQL
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 · 531 lines · 80 tokens per session scan A 713e5786e6af
data-pipeline-patterns is a skill published in the GitHub repository mickeyyaya/refactoring-skills (6 stars, last pushed 7mo ago), licensed MIT. It adds 80 tokens to every session and 4,810 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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