Nw Dr Review CriteriaSAFE
AI agents that guide you from idea to working code, with you in control at every step.
Overview
AI agents that guide you from idea to working code, with you in control at every step.
1a864ec81cdeOBSERVED · 2026-10-14What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: nw-dr-review-criteria
description: Critique dimensions, severity framework, verdict decision matrix, and review output format for documentation assessment reviews
user-invocable: false
disable-model-invocation: true
---
# 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
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
1. 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
```yaml
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.Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
1a864ec81cdefull audit observations/trust-audit/skill/nwave-ai__nw-dr-review-criteria.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-14 | 1a864ec81cde | SAFE | B | 89 | first audit |
Questions
What does the Nw Dr Review Criteria skill do?
AI agents that guide you from idea to working code, with you in control at every step.
Is Nw Dr Review Criteria safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Nw Dr Review Criteria access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (1a864ec81cde), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.