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-ad-critique-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-ad-critique-dimensions)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-ad-critique-dimensions"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-ad-critique-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-ad-critique-dimensions"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-ad-critique-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 | $0.00053 | $0.02140 |
| Opus 5 | $0.00026 | $0.01070 |
| Sonnet 5 | $0.00011 | $0.00428 |
| Haiku 4.5 | $0.00005 | $0.00214 |
Grade A, and why
nw-ad-critique-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 11d 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Acceptance Test Critique Dimensions
Load when performing peer review of acceptance tests (during *handoff-develop).
Dimension 1: Happy Path Bias
Pattern: Only successful scenarios, error paths missing.
Detection: Count success vs error scenarios. Error should be at least 40%. Missing coverage examples: login success but no invalid password | Payment processed but no decline/timeout | Search results but no empty/error cases.
Severity: blocker (production error handling untested).
Dimension 2: GWT Format Compliance
Pattern: Scenarios violate Given-When-Then structure.
Violations: Missing Given context | Multiple When actions (split into separate scenarios) | Then with technical assertions instead of business outcomes. Each scenario: Given (context), When (single action), Then (observable outcome).
Severity: high (tests not behavior-driven).
Dimension 3: Business Language Purity
Pattern: Technical terms leak into acceptance tests.
Flag: database, API, HTTP, REST, JSON, classes, methods, services, controllers, status codes (500, 404), infrastructure (Redis, Kafka, Lambda).
Business alternatives: "Customer data is stored" not "Database persists record" | "Order is confirmed" not "API returns 200 OK" | "Payment fails" not "Gateway throws exception"
Severity: high (tests coupled to implementation).
Dimension 4: Coverage Completeness
Pattern: User stories lack acceptance test coverage.
Validation: Map each story to scenarios | Verify all AC have corresponding tests | Confirm edge cases and boundaries tested.
Severity: blocker (unverified requirements).
Dimension 5: Walking Skeleton User-Centricity
Pattern: Walking skeletons describe technical layer connectivity instead of user value.
Detection litmus test for @walking_skeleton scenarios:
- Title describes user goal or technical flow?
- Then steps describe user observations or internal side effects?
- Could non-technical stakeholder confirm "yes, that is what users need"?
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.
- 11d ago First seen · 222 lines · 53 tokens per session scan A 03c12c8539b0
nw-ad-critique-dimensions is a skill published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 5d ago), licensed MIT. It adds 53 tokens to every session and 2,140 once invoked, about $0.0003 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 2026-08-30.
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