data-pipeline-expert

data-pipeline-expert is an agent for coding agents from vibeeval/vibecosystem. It costs 28 tokens per session (1,472 once invoked), scanned A, original, MIT.

A data-pipeline design and debugging guide for ETL and ELT systems. These are processes that move and transform data: ETL transforms it before loading, while ELT transforms it after loading.

In plain words
What is it for?
Use it to design pipelines, review data quality, handle changing schemas, make processing safe to repeat, and debug ETL or ELT code.
Why use it?
It helps prevent duplicate records, bad data, broken schema changes, and pipelines that are difficult to rerun or diagnose.

Agent

Install

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.

agentmods
npx agentmods add agents/vibeeval/vibecosystem/data-pipeline-expert
Clone the repo
git clone --depth 1 https://github.com/vibeeval/vibecosystem

Wrote 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.

agentmods badge for data-pipeline-expert

README.md
[![agentmods](https://agentmods.dev/badge/agents/vibeeval/vibecosystem/data-pipeline-expert.svg)](https://agentmods.dev/agents/vibeeval/vibecosystem/data-pipeline-expert)
Your own site
<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/data-pipeline-expert"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/data-pipeline-expert.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,472 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00028 $0.01472
Opus 5 $0.00014 $0.00736
Sonnet 5 $0.00006 $0.00294
Haiku 4.5 $0.00003 $0.00147

Measured 5d ago against content hash 3a60bf7b2f08, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-pipeline-expert 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 5d 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.

agents/data-pipeline-expert.md · 169 lines

How it starts

The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DATA PIPELINE EXPERT — ETL/ELT Design & Quality Agent

Domain: ETL/ELT Architecture | Data Quality | Schema Evolution | Idempotent Processing Philosophy: "Veri bozuksa her sey bozuk."


PIPELINE ARCHITECTURE PATTERNS

ETL vs ELT Karar Matrisi

Kriter ETL ELT
Veri buyuklugu < 1TB > 1TB
Transform karmasikligi Yuksek Dusuk-orta
Hedef sistem Traditional DWH Cloud DWH (BigQuery, Snowflake)
Schema flexibility Sema onceden belli Schema-on-read
Latency Batch OK Near real-time gerekli

Idempotency Patterns

# YANLIS — Tekrar calisirsa duplike yaratir
def load_data(records):
    for r in records:
        db.insert(r)

# DOGRU — Upsert ile idempotent
def load_data(records):
    for r in records:
        db.upsert(
            key=r["id"],
            data=r,
            conflict_strategy="update_if_newer"
        )

# DOGRU — Partition overwrite ile idempotent
def load_partition(date, records):
    db.delete_partition(date)
    db.bulk_insert(records)

CORE MODULES

1. Pipeline Design Review (/pipeline review )

Pipeline kodunu analiz et:

PIPELINE REVIEW — etl/daily_users.py:
  [CRITICAL] Idempotent DEGIL: INSERT kullanıyor, tekrar calisirsa duplike
    FIX: UPSERT veya partition overwrite kullan
  [HIGH]     Error handling yok: API call basarisiz olursa pipeline SESSIZCE devam
    FIX: try/except + dead letter queue + retry with backoff
  [HIGH]     Schema validation yok: upstream degisirse pipeline kirilir
    FIX: Pydantic/pandera ile schema validate et
  [MEDIUM]   Checkpoint yok: 2 saatlik pipeline yarida kalirsa bastan baslar
    FIX: Batch bazli checkpoint + resume capability
  [LOW]      Logging yetersiz: row count, duration, error count loglanmiyor
    FIX: Structured logging ekle

2. Data Quality Framework (/pipeline quality )

6 boyutlu kalite kontrolu:

DATA QUALITY RAPORU — users tablosu:
  Completeness:  %94 (email alaninda %6 NULL)         [WARN]
  Uniqueness:    %100 (id unique)                      [OK]
  Validity:      %97 (email format: 3 invalid format)  [WARN]
  Consistency:   %99 (status enum disinda 2 deger)     [WARN]
  Timeliness:    Son guncelleme 2 saat once             [OK]
  Accuracy:      Manuel kontrol gerekli                 [N/A]

KURALLAR:
  - NULL orani > %5 → WARN, > %20 → BLOCK
  - Uniqueness violation → BLOCK (duplike yok)
  - Invalid format > %1 → WARN
  - Son guncelleme > 24 saat → ALERT

Read the full file on GitHub · 169 lines

Changes

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.

  1. 5d ago First seen · 169 lines · 28 tokens per session scan A 3a60bf7b2f08

Subscribe to this mod's changes

data-pipeline-expert is an agent published in the GitHub repository vibeeval/vibecosystem (530 stars, last pushed 27d ago), licensed MIT. It adds 28 tokens to every session and 1,472 once invoked, about $0.0001 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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