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Nw Der Review Criteria

Organization
nWave-ai
nw-der-review-criteria

Evaluation criteria and scoring for data engineering artifact reviews

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-der-review-criteria
Stars
616
Forks
65
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Der Review Criteria AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-der-review-criteria
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-der-review-criteria .claude/skills/nw-der-review-criteria
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Der Review Criteria in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nw Der Review Criteria on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nw Der Review Criteria is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Data Engineer Review Criteria

Evaluation criteria for each review dimension. Load when performing reviews.

Dimension 1: Research Citation Quality

Evaluate whether recommendations trace to specific evidence.

Checks: Each major recommendation cites a specific research finding | Citations are accurate (finding number matches content) | Vendor-specific claims have multiple independent sources | General best practices distinguished from research-validated guidance

Scoring: 10: All cited, verified | 7: Most cited, 1-2 missing on non-critical points | 4: Major recommendations lack citations | 0: No citations

Dimension 2: Security Coverage

Evaluate defense-in-depth for data layer.

Checks: Encryption at rest (TDE) | Encryption in transit (TLS) | Access control model (RBAC/ABAC) | SQL injection prevention (parameterized queries) | OWASP/NIST standards referenced | Credential handling (no hardcoded secrets)

Scoring: 10: All 6 checks with standard references | 7: 4-5 checks | 4: 2-3 checks, missing encryption or injection prevention | 0: Security not mentioned

Dimension 3: Trade-off Analysis

Evaluate balanced presentation of alternatives.

Checks: Multiple technology options (minimum 2) | Pros/cons for each | Context factors identified (scale, consistency, latency, cost) | Recommendation justified by context fit | Limitations acknowledged

Scoring: 10: Comprehensive trade-offs with context-driven justification | 7: Trade-offs present, some alternatives missing | 4: Single recommendation without alternatives | 0: Prescriptive with no analysis

Dimension 4: Technical Accuracy

Checks: SQL/NoSQL syntax correct for specified DB | Architecture patterns appropriate for use case (OLTP vs OLAP, write-heavy vs read-heavy) | Optimization strategies valid for target DB | Normalization level appropriate for workload | Index type matches query patterns (B-tree for range, hash for equality) | CAP trade-offs correctly applied

Scoring: 10: All technical claims verified | 7: Minor syntax/edge-case issues | 4: Significant errors affecting recommendations | 0: Fundamentally incorrect guidance

Dimension 5: Completeness

Checks: Scaling strategy (vertical, horizontal, sharding, replication) | Performance characteristics (query patterns, bottlenecks) | Data governance when applicable (lineage, quality, MDM) | Compliance when personal/regulated data involved (GDPR, CCPA, HIPAA) | Backup/recovery for production designs | Monitoring/observability

Scoring: 10: All applicable aspects covered | 7: Core covered, 1-2 peripheral missing | 4: Major gaps (missing scaling or governance for production) | 0: Only immediate question, no broader context

Dimension 6: Bias Detection

Checks: No single-vendor preference without justification | No latest-technology bias (new tech only when justified) | Contradictory evidence acknowledged | Open-source and commercial both considered | Technology maturity/community factored in | Cost mentioned

Scoring: 10: Demonstrably balanced with explicit trade-offs | 7: Generally balanced, minor preferences | 4: Clear vendor/tech bias | 0: Single-vendor advocacy

Dimension 7: Implementability

Checks: Schema designs include column types, constraints, indexes | Architecture specifies integration points/APIs | Security has concrete steps (not just "use encryption") | Migration path described if changing systems | Dependencies/prerequisites identified | Handoff to next agent clear

Scoring: 10: Downstream agent proceeds without clarification | 7: Minor clarifications needed | 4: Significant implementation details missing | 0: Abstract guidance, no actionable content

Severity Classification Guide

Blocker: Prevents downstream work or introduces security vulnerability. Examples: missing encryption for PII, wrong DB choice for workload, SQL syntax errors in migrations.

Major: Significantly reduces quality or misses important considerations. Examples: missing trade-off analysis, no scaling strategy for production, incomplete security coverage.

Minor: Improvement that does not block progress. Examples: missing citation on secondary recommendation, single alternative not considered, minor syntax variation.

Suggestion: Enhancement that adds polish. Examples: additional index for edge-case query, governance for future compliance, alternative monitoring approach.

Frequently asked questions

What does the Nw Der Review Criteria AI skill do?

Evaluation criteria and scoring for data engineering artifact reviews

Why use Nw Der Review Criteria on TypingMind?

Because you install it once and use it with any model. Nw Der Review Criteria is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nw Der Review Criteria in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-der-review-criteria. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Der Review Criteria?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nw Der Review Criteria?

As many as you like. As long as a model supports skills, you can use Nw Der Review Criteria with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nw Der Review Criteria AI skill free?

Yes. It is published on GitHub by nWave-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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