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Nw Por Review Criteria

Organization
nWave-ai
nw-por-review-criteria

Review dimensions and bug patterns for journey artifact reviews

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-por-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 Por 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-por-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-por-review-criteria .claude/skills/nw-por-review-criteria
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Por 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 Por 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 Por 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.

Review Criteria Skill

Domain knowledge for product-owner-reviewer (Eclipse). Covers journey coherence, emotional arcs, shared artifacts, example data quality, CLI UX patterns.

Review Dimensions

Journey Coherence

Validate complete flow with no gaps.

Checks: all steps start-to-goal defined | no orphan steps | no dead ends | decision branches lead somewhere | error paths guide to recovery

Severity: critical = missing main flow steps / dead ends | high = orphan steps | medium = ambiguous decisions | low = minor clarity

Emotional Arc

Validate emotional design quality.

Checks: arc defined (start/middle/end) | all steps annotated | no jarring transitions | confidence builds progressively | error states guide not frustrate

Severity: critical = no arc / major jarring transitions | high = missing key annotations | medium = confidence doesn't build | low = minor polish

Shared Artifact Tracking

Validate ${variable} sources and consistency.

Checks: all ${variables} have documented source | single source of truth | all consumers listed | integration risks assessed | validation methods specified

Severity: critical = undocumented ${variables} / multiple sources | high = missing consumers / unassessed risks | medium = incomplete validation | low = minor consumer docs

Example Data Quality

Key review skill -- analyze data for integration gaps.

Checks: realistic not generic | reveals integration dependencies | catches version mismatches | catches path inconsistencies | consistent across steps

Severity: critical = generic placeholders hide issues | high = inconsistent across steps | medium = doesn't reveal deps | low = could be more realistic

Apply: 1) trace ${version} through all steps -- same? 2) compare ${install_path} step 2 vs 3 -- match? 3) does data show actual integration points?

Generic "v1.0.0" or "/path/to/install" hides bugs. Realistic "v1.2.86" from "pyproject.toml" reveals bugs.

CLI UX Patterns

Checks: command vocabulary consistent | help available | error messages guide to resolution | progressive disclosure respected

Severity: critical = inconsistent commands | high = no error recovery guidance | medium = missing progressive disclosure | low = minor vocabulary

Four Bug Patterns

Pattern 1: Version Mismatch

Multiple version sources. Trace ${version} through all steps -- same source?

Step 1: v${version} from pyproject.toml
Step 2: v${version} from version.txt  <-- MISMATCH

Pattern 2: Hardcoded URLs

URLs without canonical source. For each URL: "where is this defined?"

Install: git+https://github.com/org/repo
<-- Where is this URL canonically defined?

Pattern 3: Path Inconsistency

Paths from different sources. Trace ${path} -- same source?

Install to: ${install_path} from config
Uninstall from: ~/.claude/agents/nw/  <-- HARDCODED

Pattern 4: Missing Commands

CLI commands without slash equivalents. Check both contexts exist.

Terminal: crafter run
Claude Code: /nw-execute  <-- EXISTS?

Review Output Schema

yaml
review_id: "{timestamp}"
reviewer: "nw-product-owner-reviewer (Eclipse)"
artifact_reviewed: "{file path}"

strengths:
  - strength: "{Positive aspect}"
    example: "{Specific evidence}"

issues_identified:
  journey_coherence:
    - issue: "{Description}"
      severity: "critical|high|medium|low"
      location: "{Where}"
      recommendation: "{Fix}"
  emotional_arc:
    - issue: "{Description}"
      severity: "critical|high|medium|low"
      location: "{Where}"
      recommendation: "{Fix}"
  shared_artifacts:
    - issue: "{Description}"
      severity: "critical|high|medium|low"
      artifact: "{Which ${variable}}"
      recommendation: "{Fix}"
  example_data:
    - issue: "{Description}"
      severity: "critical|high|medium|low"
      data_point: "{Which data}"
      integration_risk: "{What bug it might hide}"
      recommendation: "{Fix}"
  bug_patterns_detected:
    - pattern: "version_mismatch|hardcoded_url|path_inconsistency|missing_command"
      severity: "critical|high"
      evidence: "{Finding}"
      recommendation: "{Fix}"

recommendations:
  critical: ["{Must fix before approval}"]
  high: ["{Should fix before approval}"]
  medium: ["{Fix in next iteration}"]
  low: ["{Consider for polish}"]

approval_status: "approved|rejected_pending_revisions|conditionally_approved"
approval_conditions: "{If conditional, what must be done}"

Approval Criteria

  • approved: No critical, no high issues
  • conditionally_approved: No critical, some high addressable quickly
  • rejected_pending_revisions: Critical issues exist, or multiple high

Frequently asked questions

What does the Nw Por Review Criteria AI skill do?

Review dimensions and bug patterns for journey artifact reviews

Why use Nw Por Review Criteria on TypingMind?

Because you install it once and use it with any model. Nw Por 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 Por 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-por-review-criteria. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Por 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 Por Review Criteria?

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

Is the Nw Por 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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