Claude Skill: Data Pipeline Audit

Resource overview Purpose
• Skill for reviewing scheduled jobs, ETL/ELT dependencies, idempotency, retries, observability, and data freshness.

Recommended SKILL.md contents
• Trigger description explaining when Claude should use the skill.
• Step-by-step procedure for the workflow.
• Required checks before edits or publication.
• Verification command or manual acceptance checklist.
• Links to supporting examples, scripts, or templates when the workflow needs them.

What it helps you do

Use this skill because it prevents quiet data failures in analytics and AI ingestion workflows.

Skills load their full instructions only when used, which keeps normal session context smaller.

A reusable `SKILL.md` gives teams a versioned workflow instead of repeated ad hoc prompts.

Best fit users

data engineers, backend teams, AI ingestion teams, and analytics engineers.

Teams standardizing AI-assisted delivery across multiple repositories.

Builders who want Claude to apply consistent procedures without repasting instructions.

What to know first

pipeline DAG/jobs, data sources, expected freshness, retry policy, and downstream consumers.

A skill directory such as `.claude/skills/<skill-name>/SKILL.md` or `~/.claude/skills/<skill-name>/SKILL.md`.

A concise frontmatter description so Claude can decide when to load the skill.

Environment needed

pipeline code/config access, scheduler logs, database/warehouse metadata, and safe data inspection policy.

Claude Code skill support and access to the files or commands the workflow needs.

Any external APIs, MCP servers, credentials, or local runtimes required by the specific workflow.