Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nWave-ai/nWave --skill nw-sc-review-dimensionsgit clone --depth 1 https://github.com/nWave-ai/nWaveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nwave-ai/nwave/nw-sc-review-dimensions)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-sc-review-dimensions"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-sc-review-dimensions/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-sc-review-dimensions"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-sc-review-dimensions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.02991 |
| Opus 5 | $0.00014 | $0.01496 |
| Sonnet 5 | $0.00006 | $0.00598 |
| Haiku 4.5 | $0.00003 | $0.00299 |
Grade A, and why
nw-sc-review-dimensions scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Quality Critique Dimensions
When invoked in review mode, apply these critique dimensions to production code and tests.
Persona shift: from implementer (build solutions) to independent peer reviewer (critique solutions). Focus: detect implementation bias | test quality issues | acceptance criteria coverage gaps. Mindset: fresh perspective with critical analysis - assume nothing, verify everything.
Return complete YAML feedback to calling agent for display to user.
Dimension 1: Implementation Bias Detection
Over-Engineering (YAGNI Violations)
Pattern: features, abstractions, or infrastructure without corresponding acceptance criteria.
Examples: Caching layer without performance AC | Generic framework for single use case | Premature abstraction before Rule of Three | Design patterns without demonstrated complexity need | Infrastructure (queues, workers) without scale requirement.
Detection: Compare implementation against AC | Check if feature requested by stakeholder or assumed by developer | Verify performance requirements exist before optimization | Validate abstractions serve 3+ concrete cases.
Severity: Medium to High.
Premature Optimization
Pattern: performance optimization without measurement proving necessity.
Examples: Custom caching without latency tests showing need | Complex O(log n) algorithms when simple O(n) meets AC | Memory optimizations without profiling data | Database denormalization without query analysis.
Detection: check for performance tests | verify AC specify thresholds | look for profiling data. Severity: Medium.
Solving Assumed Problems
Pattern: implementing solutions for problems not in acceptance criteria.
Examples: Multi-tenancy when AC specify single-tenant | Internationalization when AC require English only | Audit logging when AC don't mention compliance.
Detection: map each feature to corresponding AC, flag features without traceability. Severity: Medium to High.
Dimension 2: Test Quality Validation
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 310 lines · 28 tokens per session scan A ee6a365cda50
nw-sc-review-dimensions is a skill published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 6d ago), licensed MIT. It adds 28 tokens to every session and 2,991 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-06.
Other skills, from other repositories
pr-review-triage
Watch open PRs, check CI status, review staleness, merge conflicts, and unanswered review comments. Produces a prioritized watchlist.
learn-from-sage
Detection-gap (miss) analysis for Code Review Sage. Learn from shipped fixes, acted-on human comments, and design outcomes to close reviewer blind spots. Inline during review stages a candidate; a human triggers a one-shot AI consolidation into the live ruleset.
plan-implementation
Disciplined execution of approved plans with step-by-step verification, phase checkpoints, failure investigation, and mandatory code/security reviews.
security-review
Security vulnerability assessment identifying OWASP risks, injection vectors, authentication issues, and data exposure with severity classification.
verification
Verification-before-completion discipline ensuring all success criteria are met, tests pass, and reviews complete before declaring work done.
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements.