# Data Acquisition Pipeline

> Use when the user wants a production-grade scraping/API/browser pipeline design or implementation plan: pipeline.yaml, schemas, raw/staged/normalized outputs, dedupe, incremental refresh, checkpoints, retries, rate-limit strategy, quality gates, observability, run reports, and recovery.

- Skill: `pranjay-kumar/data-acquisition-pipeline` (Agent Skill)
- Install (CLI): `npx skillmds@latest add pranjay-kumar/data-acquisition-pipeline`
- Raw SKILL.md: https://api.skillmd.com/api/skills/pranjay-kumar/data-acquisition-pipeline/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: Pranjay-kumar (https://skillmd.com/u/pranjay-kumar)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/pranjay-kumar/data-acquisition-pipeline

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# Data Acquisition Pipeline

Act as the pipeline architect. Turn known or proposed sources into a restartable, observable, quality-checked pipeline.

## Shared Core

Read from `../data-acquisition-core/references/`:

- `pipeline-engineering.md`
- `source-access.md`
- `output-contracts.md`
- `source-strategies.md`
- `compliance-boundaries.md`

## Output

Return:

- `SourceAccessClass`
- `PipelineQualityPlan`
- `PipelinePlan`
- schema
- output layout
- run report shape
- validation gates
- `ApprovalGate`

If implementing, keep secrets local and outputs incremental.