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data-engineering

Use for ingestion, ETL or ELT, data pipelines, mapping, cleansing, validation, reconciliation, lineage, warehousing, dimensional models, semantic layers, file…

Skill by SiddheshKGupta

IndexedApache-2.0★ 4
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Install

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/siddheshkgupta-data-engineering

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Use for ingestion, ETL or ELT, data pipelines, mapping, cleansing, validation, reconciliation, lineage, warehousing, dimensional models, semantic layers, file or API ingestion, data-quality monitoring, SQL or reporting datasets. Do not use for a simple isolated query with no reusable data flow.

Use cases: backend, data

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Permissions

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

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/data-engineering

What's inside

Skill name
data-engineering
Description
Use for ingestion, ETL or ELT, data pipelines, mapping, cleansing, validation, reconciliation, lineage, warehousing, dimensional models, semantic layers, file or API ingestion, data-quality monitoring, SQL or reporting datasets. Do not use for a simple isolated query with no reusable data flow.
License
Apache-2.0
Instructions
25 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Data Engineering

For each dataset define:

```text
Source | Owner | Extraction | Frequency | Schema | Validation
Transformation | Deduplication | Enrichment | Storage | Lineage
Reconciliation | Serving | Retention | Access | Monitoring | Failure handling
```

Procedure:

1. Confirm the source of truth and volume.
2. Define idempotent ingestion and schema handling.
3. Validate completeness, validity, uniqueness, consistency, timeliness,
   accuracy, referential integrity and reconciliation.
4. Record rejected rows rather than silently dropping them.
5. Separate masters, transactions, events, documents and reporting aggregates.
6. Make transformations and lineage traceable.
7. Define control totals, freshness and recovery.
8. Verify with representative and failure data.

Describe repeatable large-scale data work as a pipeline, not merely analysis.

From SiddheshKGupta/Escapement, Apache-2.0.

More from SiddheshKGupta/Escapement

Signals

Stars
4
Forks
1
Open issues
0
Last commit (this folder)
Aug 5, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
Repository
github.com/SiddheshKGupta/Escapement
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

More from SiddheshKGupta

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Cite this page

data-engineering skill. Agent Mart. https://agentmart.dev/skills/siddheshkgupta-data-engineering (last updated 2026-10-24)