# Data workflow — SkillDB starter bundle

Development preview · Evaluation pending

Plan a repeatable batch import, define what each row means, and make bad data visible.

For a small analytical data pipeline. Select the simplest suitable storage and runtime; no infrastructure is created.

Selection review: 2026-10-04. Selection reviewed for topic coverage and overlap. Code examples and task outcomes have not been evaluated.
Maintainer provenance: private repository latentsmurf/SkillDB, revision 0a89389d0ec3e4f12e694baab71bdb56134eeb7e. Maintainer access required; this is not a public source link. Catalog pages may change after the selection review.

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Reference manifest only: selection notes, skill IDs, and public catalog links. No skill bodies, executable code, credentials, or host configuration are included. Downloading does not install or enable skills.

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This is a reading and review plan, not an agent skill or host configuration. Start with the public catalog pages and review available instructions before use.

## Why these skills fit together

Data Pipeline Architecture covers movement and recovery, Data Modeling covers grain and relationships, and Data Quality covers validation. All discuss contracts; write one shared contract rather than maintaining three versions.

## 1. Data Pipeline Architecture

Role: Pipeline and recovery plan

Covers batch versus streaming choices, idempotency, schema changes, and recoverable failures.

Review note: The scale and latency examples are illustrative. Do not adopt streaming infrastructure or exactly-once claims without testing the actual source and sink.

Skill ID: data-engineering-skills/data-pipeline-architecture.md
Public catalog: https://skilldb.dev/skills/data-engineering-skills/data-pipeline-architecture
Maintainer provenance path (private repository; maintainer access required): packs/data-engineering-skills/data-pipeline-architecture.md

## 2. Data Modeling

Role: Row grain and relationships

Explains grain, keys, and model choices so the pipeline produces data that supports the intended questions.

Review note: Enterprise modeling approaches are alternatives, not a checklist to implement. A small workflow may need only a clearly documented table.

Skill ID: data-engineering-skills/data-modeling.md
Public catalog: https://skilldb.dev/skills/data-engineering-skills/data-modeling
Maintainer provenance path (private repository; maintainer access required): packs/data-engineering-skills/data-modeling.md

## 3. Data Quality

Role: Validation and failure visibility

Adds completeness, uniqueness, freshness, and reconciliation checks around the pipeline contract.

Review note: Choose thresholds from the dataset and business rules. The example percentages and composite scores are not validated targets for your project.

Skill ID: data-engineering-skills/data-quality.md
Public catalog: https://skilldb.dev/skills/data-engineering-skills/data-quality
Maintainer provenance path (private repository; maintainer access required): packs/data-engineering-skills/data-quality.md

## Suggested evaluation — not yet run

Import a small orders dataset into a reporting table, then repeat the import with duplicate rows, missing fields, and a late correction.

- [ ] Define row grain and a repeatable key before implementing transformations.
- [ ] Run the same import twice and verify that intended totals remain stable.
- [ ] Make rejected records, freshness, and reconciliation results visible; test a recovery or backfill.

Compare the same task with and without these references using the same environment and checks. Record outcomes before claiming an improvement.