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data-pipeline-quality

Design or review data pipeline correctness, lineage, quality, and replay behavior.

Skill by Sertxito

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/sertxito-data-pipeline-quality

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About

Design or review data pipeline correctness, lineage, quality, and replay behavior. Use for ingestion, transformation, ETL/ELT, or batch/stream processing.

Use cases: design, data

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/data-pipeline-quality

What's inside

Skill name
data-pipeline-quality
Description
Design or review data pipeline correctness, lineage, quality, and replay behavior. Use for ingestion, transformation, ETL/ELT, or batch/stream processing.
License
MIT
Instructions
23 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Data pipeline quality

## Procedure
1. Identify authoritative inputs, schema versions, owners, and downstream consumers.
2. Trace transformations and define lineage and expected invariants.
3. Specify freshness, completeness, uniqueness, validity, and reconciliation checks.
4. Define retry, idempotency, checkpoint, replay, and backfill behavior.
5. Add safe representative fixtures and report sensitive-data constraints.

## Operational data contract
Bound batch/backfill sizes, event-time/processing-time assumptions, late or
out-of-order records, deduplication keys and schema evolution across producers
and consumers. Specify reconciliation tolerances and a quarantine owner instead
of silently dropping invalid rows.
Record job/checkpoint ownership, retries, timeout/concurrency, dead-letter
retention and replay side effects. Use synthetic referentially valid fixtures;
verify recovery from a partial batch before claiming exactly-once behavior.

## Output
Source-target map, invariants, lineage, quality gates, recovery plan, and
observability signals. Do not copy production data into tests without approval.

From Sertxito/mcp-efficiency-engine, MIT.

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Last commit (this folder)
Oct 22, 2026 (6 days ago)
Last release
Oct 22, 2026 (6 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
Repository
github.com/Sertxito/mcp-efficiency-engine
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

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Cite this page

data-pipeline-quality skill. Agent Mart. https://agentmart.dev/skills/sertxito-data-pipeline-quality (last updated 2026-10-24)