What makes Data Pipeline Design Review notable?

990 downloads. Trust evidence available

How should Data Pipeline Design Review be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Data Pipeline Design Review

Use when a data engineer needs a structured design review of a proposed data pipeline, ETL/ELT flow, or dbt/SQL model before it ships. Produces severity-rate... Skill: Data Pipeline Design Review Owner: archlab-space Summary: Use when a data engineer needs a structured design review of a proposed data pipeline, ETL/ELT flow, or dbt/SQL model before it ships. Produces severity-rate... Tags: latest:0.1.3 Version history: v0.1.3 | 2026-05-28T09:47:08.275Z | user Version 0.1.3 v0.1.1 | 2026-05-21T12:53:25.051Z | user Added - Feedback prompt in README.md and conditional feedback

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

990

Updated

Oct 11, 2026

Version

0.1.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 990 downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 990 downloadsadoption · observed Oct 11, 2026
Latest release: 0.1.3release · observed May 28, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 0.1.3release
Version 0.1.3
Observed May 28, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s176qz6rwtpzj9gk93r7b3jm6984ty2d:data-pipeline-design-review

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-archlab-space-data-pipeline-design-review/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

27,194 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: data-pipeline-design-review
description: Use when a data engineer needs a structured design review of a proposed data pipeline, ETL/ELT flow, or dbt/SQL model before it ships. Produces severity-rated findings across correctness, idempotency, data quality, schema evolution, observability, and cost, plus a remediation checklist and a go/no-go recommendation.
---

# Data Pipeline Design Review

You are a senior data platform reviewer. Your job is to pressure-test a proposed pipeline or transformation design and surface the reliability, data-quality, and cost failures that usually only appear in production — before it ships. You review the design; you do not rewrite it unless asked.

## Flow

1. **Intake.** Collect the design. Ask, one question at a time, only for what is missing:
   - Sources (system, format, volume, arrival pattern, late/duplicate data behavior)
   - Transformations (engine, language, key joins/aggregations)
   - Sink/target (table, storage, partitioning, consumers and their SLAs)
   - Orchestration (scheduler, frequency, backfill strategy, retries)
   - Failure expectations (what happens on partial failure, reprocessing, replay)
   Accept a free-form design doc or a dbt/SQL model directly. Do not block on perfect input — note missing context as an assumption and proceed.
2. **Classify the artifact** and route the review depth:
   - **Architecture description** → emphasize correctness, idempotency, schema evolution, cost.
   - **dbt/SQL model** → also inspect materialization, incremental predicates, grain, tests, fan-out joins.
   - **Streaming flow** → also inspect ordering, watermarking, exactly/at-least-once semantics, backpressure.
3. **Review across the six dimensions** (every review must cover all six):
   1. **Correctness & grain** — join fan-out, double counting, time-zone/late-data handling, deduplication, primary-key integrity.
   2. **Idempotency & recovery** — safe re-run, partial-failure behavior, backfill/replay, exactly-vs-at-least-once.
   3. **Data quality** — null/range/uniqueness/referential checks, freshness SLAs, contract with upstream, quarantine path for bad rows.
   4. **Schema evolution** — additive vs breaking changes, contract enforcement, consumer impact, versioning.
   5. **Observability** — lineage, run metrics, alerting on freshness/volume anomalies, debuggability of a single bad record.
   6. **Cost & performance** — partition/cluster strategy, full-vs-incremental scans, shuffle/skew, redundant recomputation.
4. **Rate each finding** Critical / High / Medium / Low (see severity rubric) and tie it to a concrete failure scenario.
5. **Produce the report** in the Output Format, ending with a go/no-go recommendation and an ordered remediation checklist.

## Severity Rubric

- **Critical** — silent data corruption, non-idempotent reprocessing, or permanent data loss is possible. Blocks ship.
- **High** — wrong results or pipeline outage under a realistic, foreseeable condition. Blocks ship unles

README.md

# Data Pipeline Design Review

**Platforms:** Claude · Codex
**Domain:** Data Engineering

## Purpose

Pressure-tests a proposed data pipeline, ETL/ELT flow, or dbt/SQL model and surfaces the reliability, data-quality, schema-evolution, and cost failures that normally only appear in production. Produces severity-rated findings, a remediation checklist, and a go/no-go recommendation before the design ships.

## When to Use

- Before merging a new pipeline or dbt model into production
- During a design review or architecture sign-off for a data flow
- When a backfill, replay, or reprocessing strategy needs a second opinion
- When investigating why a pipeline produces duplicate, missing, or wrong results

## What It Does

**Phase 1: Intake**
1. Collects sources, transformations, sink, orchestration, and failure expectations — one question at a time, treating missing context as explicit assumptions

**Phase 2: Structured Review**
2. Classifies the artifact (architecture, dbt/SQL model, or streaming flow) and routes review depth
3. Reviews all six dimensions: correctness & grain, idempotency & recovery, data quality, schema evolution, observability, cost & performance
4. Rates each finding Critical/High/Medium/Low and ties it to a concrete production failure scenario

**Phase 3: Output**
5. Produces a findings report, dimension-coverage statement, ordered remediation checklist, and a go/no-go recommendation

## Notes

The skill reviews the design rather than rewriting it. A single Critical finding forces a No-Go until resolved. Designs may reference internal systems; the skill does not store or transmit input beyond the current session.

## Feedback & Contributions

Found a gap or have a suggestion? [Open an issue or PR](https://github.com/archlab-space/Open-Skill-Hub/issues) — improvements are welcome.

_meta.json

{
  "ownerId": "kn798vfcxrgjdt230v34k8eqf584vpwv",
  "slug": "data-pipeline-design-review",
  "version": "0.1.3",
  "publishedAt": 1779961628275
}

CHANGELOG.md

# Changelog

## [0.1.2] - 2026-05-28
Rewrote frontmatter description to concise 200–500 character format for improved agent-trigger clarity.

## [0.1.1] - 2026-05-21

### Added
- Feedback prompt in README.md and conditional feedback section in SKILL.md

## [0.1.0] - 2026-05-19
Initial release. Data pipeline design review skill that produces severity-rated findings across six reliability and data-quality dimensions, a remediation checklist, and a go/no-go recommendation.

skill-card.md

## Description:

Use when a data engineer needs a structured design review of a proposed data pipeline, ETL/ELT flow, or dbt/SQL model before it ships.

This skill is ready for commercial/non-commercial use.

## Publisher:

[archlab-space](https://clawhub.ai/user/archlab-space)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and data engineers use this skill to pressure-test data pipeline, ETL/ELT, streaming, and dbt/SQL designs before release. It produces severity-rated reliability, data-quality, schema-evolution, observability, and cost findings with a remediation checklist and go/no-go recommendation.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Pipeline design reviews may include internal system names, schemas, operational details, or credentials if users paste them into the prompt.

Mitigation: Share only the design details needed for review and redact secrets, credentials, and unnecessary sensitive identifiers before use.

Risk: The skill's findings are design-review guidance and may miss issues when source, orchestration, failure, or consumer-SLA details are absent.

Mitigation: Treat missing context as assumptions, review the generated assumptions and findings, and confirm recommendations with the responsible data platform owner before release.

## Reference(s):


## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown review report with severity-rated findings, assumptions, dimension coverage, remediation checklist, and recommendation]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May ask targeted follow-up questions when key pipeline design details are missing.]

## Skill Version(s):

0.1.3 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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