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Nw Par Review Criteria

Organization
nWave-ai
nw-par-review-criteria

Quality dimensions and review checklist for devop reviews

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-par-review-criteria
Stars
616
Forks
65
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 Par 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-par-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-par-review-criteria .claude/skills/nw-par-review-criteria
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Par 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 Par 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 Par 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.

DevOp Reviewer: Review Criteria

Critique Dimension 1: Incomplete Phase Handoffs

Pattern: Phase handoffs missing required artifacts or approvals.

Required per Phase:

  • DISCUSS: Requirements document + peer review approval
  • DESIGN: Architecture document + ADRs + peer review approval
  • DISTILL: Acceptance tests + peer review approval
  • DELIVER: Production code + tests (100% passing) + peer review approval

Severity: critical. Verify all artifacts present and peer-reviewed before phase transition.

Critique Dimension 2: Deployment Readiness Gaps

Pattern: Feature marked "ready" but missing production prerequisites.

Required: All tests passing (100%) | Production configuration complete | Monitoring/alerting configured | Runbook/operational docs created | Rollback plan documented.

Severity: critical. Complete missing prerequisite before marking deployment-ready.

Critique Dimension 3: Traceability Violations

Pattern: Cannot trace production code back to requirements.

Required: User stories map to acceptance tests | Acceptance tests map to production code | Code changes traceable to commits | All AC verified in production.

Severity: high. Establish traceability chain: user-story -> acceptance-tests -> code-commits.

Critique Dimension 4: Priority Validation

Purpose: Validate roadmap addresses largest bottleneck first, not secondary concern.

Questions

Q1: Is this the largest bottleneck? Does timing data show primary problem? Larger problem being ignored? Assessment: YES / NO / UNCLEAR.

Q2: Were simpler alternatives considered? Roadmap includes rejected alternatives? Rejection reasons evidence-based? Simpler solution achieves 80% benefit? Assessment: ADEQUATE / INADEQUATE / MISSING.

Q3: Is constraint prioritization correct? Constraints quantified by impact? Architecture addresses constraint-free opportunities first? Minority constraint dominating? (flag if >50% of solution for <30% of problem). Assessment: CORRECT / INVERTED / NOT_ANALYZED.

Q4: Is architecture data-justified? Key architectural decision supported by quantitative data? Different data leads to different architecture? Assessment: JUSTIFIED / UNJUSTIFIED / NO_DATA.

Failure Conditions

  • FAIL if Q1 = NO (wrong problem being addressed)
  • FAIL if Q2 = MISSING (no alternatives considered)
  • FAIL if Q3 = INVERTED (minority constraint dominating)
  • FAIL if Q4 = NO_DATA and this is performance optimization

Critique Dimension 5: Functional Integration

Purpose: Verify feature wired into system entry point -- prevents Testing Theatre. A feature with 100% test coverage but 0% wiring tests is not complete.

Validation Criteria:

  1. Wiring test exists: at least one acceptance test invokes feature through driving port
  2. Component integrated: implemented component called from entry point module
  3. Boundary correct: acceptance tests do not import internal components directly

Gate failure response: Block finalization | report specific integration gap with evidence | require integration step before completion.

Quality Gate Checklist

Technical Completion

  • All acceptance tests passing with stakeholder validation
  • Unit test coverage meeting project standards (>=80%)
  • Integration test validation of cross-component functionality
  • Code review completed with approval
  • Static analysis and security scan passed
  • Performance tested under realistic load

Architecture Compliance

  • Implementation aligns with architectural design
  • Component boundaries and interfaces respected
  • Security architecture implemented correctly

Production Readiness

  • Monitoring and alerting configured
  • Logging and debugging capability validated
  • Rollback procedure documented and tested
  • Operational runbook complete
  • Support team trained / knowledge transferred

Business Completion

  • All user stories completed with acceptance criteria met
  • Business rules implemented and validated
  • Stakeholder acceptance obtained

Frequently asked questions

What does the Nw Par Review Criteria AI skill do?

Quality dimensions and review checklist for devop reviews

Why use Nw Par Review Criteria on TypingMind?

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

Which AI models can use Nw Par 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 Par Review Criteria?

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

Is the Nw Par 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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