nw-dr-review-criteria

Solid

Critique dimensions, severity framework, verdict decision matrix, and review output format for documentation assessment reviews

Code & Development 526 stars 55 forks Updated 1 weeks ago MIT

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Quality Score: 92/100

Stars 20%
91
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Documentation Review Criteria ## Critique Dimensions ### 1. Classification Accuracy Verify type assignment against DIVIO decision tree. Questions: Do cited signals support assigned type? | Contradicting signals ignored? | Confidence appropriate? | Decision tree leads to same classification? Verification: 1) Run decision tree independently 2) Check positive signals present 3) Check for red flags 4) Verify confidence matches signal strength Severity: if wrong classification leads to wrong verdict = blocking. ### 2. Validation Completeness Verify all type-specific criteria checked. Questions: All items checked? | Pass/fail correct? | Issues properly located? | Any criteria missed? **Tutorial** (required): completable without external refs | steps numbered/sequential | verifiable outcomes | no assumed knowledge | builds confidence **How-to** (required): clear goal | assumes fundamentals | single task | completion indicator | no basics teaching **Reference** (required): all params documented | return values | error conditions | examples | no narrative **Explanation** (required): addresses "why" | context/reasoning | alternatives considered | no task steps | conceptual model ### 3. Collapse Detection Correctness Verify all five anti-patterns checked with accurate findings. - Tutorial creep: explanation >20% | How-to bloat: teaching basics | Reference narrative: prose in entries - Explanation task drift: steps in explanation | Hybrid horror: 3+ quadrants Verificatio...

Details

Author
nWave-ai
Repository
nWave-ai/nWave
Created
3 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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