Documentation Review Criteria
SkillDocs & knowledgeCritique dimensions, severity framework, verdict decision matrix, and review output format for documentation assessment reviews
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Documentation Review Criteria skill
What this skill tells your AI
The instructions your AI receives, as published by nwave-ai/nwave in nWave/skills/nw-dr-review-criteria/SKILL.md and read by ahel’s review.
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
Verification: independently scan, count lines per quadrant, compare to documentarist's findings, flag discrepancies.
4. Recommendation Quality
Criteria: Specific (exact what/where) | Actionable (author knows next step) | Prioritized (important first) | Justified (why it matters) | Root cause (underlying issue)
Bad: "Improve the documentation", "Make it clearer" Good: "Move explanation in section 3.2 (lines 45-60) to separate doc", "Add return value docs for login()"
5. Quality Score Accuracy
Verify six characteristics: Accuracy (factual claims verified?) | Completeness (gap analysis thorough?) | Clarity (Flesch 70-80?) | Consistency (style 95%+?) | Correctness (errors counted?) | Usability (structural assessment?)
Note: Documentarist cannot fully measure accuracy (needs expert) or usability (needs user testing). Verify limitations properly scoped.
6. Verdict Appropriateness
Verify verdict matches findings per decision matrix below.
Severity Framework
| Level | Definition | Action |
|---|---|---|
| Blocking | Wrong classification/verdict, missed collapse making doc unusable | Must fix |
| High | Multiple criteria missed, collapse missed but usable | Should fix; may block |
| Medium | Single criterion missed, miscalibrated confidence, false positive | Recommended |
| Low | Format inconsistency, wording clarity | Optional |
Reject: any blocking | 3+ high | classification wrong | verdict contradicts findings Conditionally approve: 1-2 high not affecting verdict | multiple medium but core correct Approve: no blocking/high | medium noted but not blocking
Verdict Decision Matrix
- Approved: all checks pass or low-only failures | no collapse | quality gates met (Flesch 70-80, purity 80%+)
- Needs Revision: medium/low failures only | no collapse | fixable without restructuring
- Restructure Required: collapse detected | purity <80% | multiple user needs | requires splitting
Verification Algorithm
- Count issues by severity 2. Check collapse_detection.clean 3. Check quality gates 4. Apply matrix 5. Compare to documentarist verdict 6. Flag discrepancy
Review Output Format
documentation_assessment_review:
review_id: "doc_rev_{timestamp}"
reviewer: "nw-documentarist-reviewer (Quill)"
assessment_reviewed: "{path}"
original_document: "{path}"
classification_review:
accurate: [boolean]
confidence_appropriate: [boolean]
independent_classification: "[your type]"
match: [boolean]
issues: [{issue, evidence, severity, recommendation}]
validation_review:
complete: [boolean]
criteria_checked: "[X/Y required + Z/W additional]"
missed_criteria: [list]
issues: [{issue, severity, recommendation}]
collapse_detection_review:
accurate: [boolean]
independent_findings: "[anti-patterns found]"
false_positives: [count]
missed_patterns: [list]
issues: [{issue, severity, recommendation}]
recommendation_review:
quality: [high|medium|low]
actionable: [boolean]
properly_prioritized: [boolean]
issues: [{issue, severity, improvement}]
quality_score_review:
accurate: [boolean]
issues: [{score, issue, correction}]
verdict_review:
appropriate: [boolean]
documentarist_verdict: "[their verdict]"
recommended_verdict: "[your verdict]"
verdict_match: [boolean]
rationale: "{justification}"
overall_assessment:
assessment_quality: [high|medium|low]
approval_status: [approved|rejected_pending_revisions|conditionally_approved|escalate_to_human]
issue_summary: {blocking: N, high: N, medium: N, low: N}
blocking_issues: [list]
recommendations: [{priority, action}]
Review Iteration Limits
Maximum 2 revision cycles. After cycle 2: escalate to human, return approval_status: escalate_to_human with rationale.
Signals
- GitHub stars
- 610
- Forks
- 63
- Last commit
- Sep 2026
Advanced
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nw-dr-review-criteria- Source
- github.com/nwave-ai/nwave