Nw Ab Critique Dimensions logo

Nw Ab Critique Dimensions

Organization
nWave-ai
nw-ab-critique-dimensions

Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-ab-critique-dimensions
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 Ab Critique Dimensions 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-ab-critique-dimensions
  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-ab-critique-dimensions .claude/skills/nw-ab-critique-dimensions
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Ab Critique Dimensions 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 Ab Critique Dimensions 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 Ab Critique Dimensions 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.

Agent Quality Critique Dimensions

Use these dimensions when reviewing or validating agent definitions.

Dimension 1: Template Compliance

Does the agent follow official Claude Code format?

Check: YAML frontmatter with name and description (required) | Markdown body as system prompt | No embedded YAML config blocks | No activation-instructions or IDE-FILE-RESOLUTION sections | Skills referenced in frontmatter, not inline

Severity: High -- non-compliant agents may not load correctly.

Dimension 2: Size and Focus

Check: Core definition under 400 lines | Domain knowledge in Skills | Single clear responsibility | No monolithic sections (>50 lines without structure) | No redundant Claude default behaviors

Measurement: wc -l {agent-file}. Target: 200-400 lines.

Severity: High -- oversized agents suffer context rot.

Dimension 3: Divergence Quality

Does the agent specify only what diverges from Claude defaults?

Check: No file operation instructions | No generic quality principles ("be thorough") | No tool usage guidelines | Core principles are domain-specific and non-obvious | Each instruction justifies why Claude wouldn't do this naturally

Severity: Medium -- redundant instructions waste tokens, cause overtriggering.

Dimension 4: Safety Implementation

Check: Tools restricted via frontmatter tools field | maxTurns set | No prose-based security layers (use hooks) | No embedded enterprise safety frameworks | permissionMode set for risky actions

Severity: High -- prose safety is ineffective and token-wasteful.

Dimension 5: Language and Tone

Check: No "CRITICAL:", "MANDATORY:", "ABSOLUTE" language | Direct statements ("Do X" not "You MUST X") | Affirmative phrasing ("Do Y" not "Don't do X") | Consistent terminology | No repetitive emphasis

Severity: Medium -- aggressive language causes overtriggering on Opus 4.6.

Dimension 6: Examples Quality

Check: 3-5 canonical examples present | Cover critical/subtle decisions (not obvious cases) | Good/bad paired where useful | Concise (not full implementations)

Severity: Medium -- missing examples cause edge case failures.

Dimension 7: Skill Loading Effectiveness

Does the agent ensure skills are actually loaded during execution?

Check: Skill Loading Strategy table present for agents with 3+ skills | Every frontmatter skill has matching Load: directive in workflow | Skills path documented (~/.claude/skills/nw-{skill-name}/SKILL.md) | Phase-gated loading (not "load everything at start")

Severity: High — orphan skills (declared but never loaded) mean sub-agents operate without domain knowledge. The skills: frontmatter field is declarative only; Claude Code does not auto-load skill files.

Gold standard: nw-product-owner.md — Skill Loading Strategy table mapping phases to skills with triggers + explicit Load: directives in each workflow phase.

Dimension 8: Token Efficiency

Is the agent definition compressed without losing semantic content?

Check: No verbose prose where pipe-delimited lists suffice | Imperative voice throughout | No filler words ("in order to", "it is important to") | ### Example N: headers preserved verbatim (not inlined) | AskUserQuestion options preserved with numbered descriptions | Code blocks preserved verbatim | No duplicate content already in skills

Severity: Medium — bloated definitions waste context window and degrade performance via context rot.

Compression safe: prose descriptions, bullet lists, related items → pipe-delimited Compression unsafe: example headers, code blocks, decision tree options, YAML frontmatter

Dimension 9: Priority Validation

Questions: 1. Is this the largest bottleneck? (Evidence required) | 2. Simpler alternatives considered? | 3. Constraint prioritization correct? | 4. Architecture data-justified?

Severity: High if agent addresses secondary concern while larger problem exists.

Review Output Format

yaml
review:
  agent: "{agent-name}"
  dimensions:
    template_compliance: {pass|fail}
    size_and_focus: {pass|fail}
    divergence_quality: {pass|fail}
    safety_implementation: {pass|fail}
    language_and_tone: {pass|fail}
    examples_quality: {pass|fail}
    skill_loading: {pass|fail|n/a}
    token_efficiency: {pass|fail}
    priority_validation: {pass|fail}
  issues:
    - dimension: "{dimension}"
      severity: "{high|medium|low}"
      finding: "{description}"
      recommendation: "{fix}"
  verdict: "{approved|revisions_needed}"

Failure Conditions

Review blocked (verdict: revisions_needed) if: any high-severity dimension fails | 3+ medium-severity fail | Agent exceeds 400 lines without Skills extraction | Zero examples provided | Agent with 3+ skills missing Skill Loading Strategy table

Frequently asked questions

What does the Nw Ab Critique Dimensions AI skill do?

Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation

Why use Nw Ab Critique Dimensions on TypingMind?

Because you install it once and use it with any model. Nw Ab Critique Dimensions 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 Ab Critique Dimensions in TypingMind?

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

Which AI models can use Nw Ab Critique Dimensions?

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 Ab Critique Dimensions?

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

Is the Nw Ab Critique Dimensions 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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