Nw Po Review DimensionsSAFE
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-po-review-dimensions description: Requirements quality critique dimensions for peer review - confirmation bias detection, completeness validation, clarity checks, testability assessment, and priority validation user-invocable: false disable-model-invocation: true --- # Requirements Quality Critique Dimensions When invoked in review mode, apply these critique dimensions to requirements documents. Persona shift: from requirements analyst to independent requirements reviewer. Focus: detect confirmation bias | validate completeness | ensure clarity and testability. Mindset: fresh perspective -- assume nothing, challenge assumptions, verify stakeholder needs. Return complete YAML feedback to calling agent for display to user. --- ## Dimension 0: Elevator Pitch Test (BLOCKING, checked first) Every user story MUST contain an `### Elevator Pitch` subsection with three lines: Before / After / Decision enabled. The reviewer checks these invariants: 1. **Presence**: the section exists with all three lines. Missing → **BLOCKING**. 2. **Real entry point**: the "After" line references a user-invocable entry point (CLI subcommand, HTTP endpoint path, UI action) — not a service function, internal API, or test runner command. Internal-only → **BLOCKING**. 3. **Concrete output**: the "sees" clause describes observable output (stdout text sample, HTTP response body shape, rendered screen element) — not internal state, "tests pass", or "data is persisted". Internal state → **BLOCKING**. 4. **Job connection**: the "Decision enabled" line names a real decision the user makes with the output. If the story enables no user decision, it is infrastructure — BLOCK with recommendation to merge into a value-producing story. 5. **Slice-level check**: if every story in a slice is `@infrastructure`, the slice has no release value. **BLOCKING** at slice level — recommend re-slicing so that each slice contains at least one user-visible story. Return this dimension first in the YAML feedback. If any BLOCKING issue is found, the overall review verdict MUST be `BLOCKED` regardless of other dimensions. --- ## Dimension 1: Confirmation Bias Detection ### Technology Bias Pattern: requirements assume specific technology without stakeholder requirement. Examples: "Deploy to AWS" when deployment not discussed | "Use PostgreSQL" in requirements instead of architecture. Detection: check for technology specifics (cloud, database, frameworks). Verify stakeholder interviews mentioned these. Severity: HIGH (constrains solution space unnecessarily). ### Happy Path Bias Pattern: requirements focus on successful scenarios, minimal error/exception coverage. Examples: login documented but account lockout missing | payment success but fraud/timeout/decline not specified. Detection: count happy path stories vs error scenarios. Check each story has "sad path" alternatives. Severity: CRITICAL (incomplete requirements, production error handling missing). ### Availability Bias Pattern: requirements reflect recent experiences or familiar patterns over comprehensive analysis. Examples: "Same auth as previous project" without validating fit | requirements mirror competitor without stakeholder validation. Detection: check if requirements justified by stakeholder needs or "like previous project." Severity: MEDIUM (sub-optimal solution, missed opportunities). --- ## Dimension 2: Completeness Validation ### Missing Stakeholder Perspectives Stakeholder groups to verify: end users (primary, secondary, occasional) | business owners/sponsors | operations/support teams | compliance/legal | technical teams. Detection: list stakeholder groups in requirements, check each group's needs represented, verify conflicting needs documented. Severity: HIGH. ### Missing Error Scenarios Required: invalid input validation | authentication/authorization failures | network timeouts | external service unavailability | data integrity violations | concurrent modification conflicts | resource exhaustion. Detection: for each user story, check for corresponding error scenarios. Severity: CRITICAL. ### Missing Non-Functional Requirements NFRs to validate: performance (latency, throughput) | security (auth, data protection) | scalability (concurrent users, data volume) | reliability (uptime, error rates) | compliance (regulatory, legal) | accessibility (WCAG). Detection: check NFR section exists, each NFR has measurable criteria, stakeholders provided expectations. Severity: CRITICAL. --- ## Dimension 3: Clarity and Measurability ### Vague Performance Requirements Pattern: qualitative terms without quantitative thresholds. Vague: "System should be fast" | "User-friendly interface" | "Handle large volumes" | "Highly available." Detection: identify qualitative adjectives (fast, large, friendly, high, secure). Check for corresponding quantitative threshold. Severity: HIGH. ### Ambiguous Requirements Pattern: requirements interpretable multiple ways. Detection: check if two architects could design differently from same requirements. Look for multi-meaning words. Verify pronouns have clear antecedents. Severity: HIGH. --- ## Dimension 4: Testability ### Non-Testable Acceptance Criteria Pattern: AC not observable, measurable, or automatable. Bad: "System should be easy to use" | "Code should be maintainable." Good: "User completes checkout in 3 or fewer clicks, 95% success rate" | "Cyclomatic complexity at most 10, test coverage at least 80%." Detection: for each AC, ask "Can an automated test verify this?" Check if AC specifies observable behavior with measurable pass/fail. Severity: CRITICAL. --- ## Dimension 5: Priority Validation ### Questions to Ask **Q1: Is this the largest bottleneck?** Does timing data show this is the primary problem? Is there a larger problem being ignored? **Q2: Were simpler alternatives considered?** Does the document include rejected alternatives? Are rejection reasons evidence-based? **Q3: Is constraint prioritiza
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-po-review-dimensions.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 Po Review Dimensions skill do?
AI agents that guide you from idea to working code, with you in control at every step.
Is Nw Po Review Dimensions 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 Po Review Dimensions 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.