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-test-optimizationgit 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-test-optimization)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-test-optimization"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-test-optimization/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-test-optimization"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-test-optimization.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 | $0.00033 | $0.05068 |
| Opus 5.5 | $0.00013 | $0.02027 |
| Sonnet 5.5 | $0.00007 | $0.01014 |
| Haiku 4.5 | $0.00003 | $0.00507 |
Grade A, and why
nw-test-optimization 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 1mo 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 — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Optimization Methodology
Mission
Minimize tests, maximize value, reduce feedback time, maintain quality. (Ale, 2026-04-28: "Bisogna minimizzare i test, massimizzare il valore per ridurre il tempo di feedback, mantenendo la qualità.")
This skill operationalizes that mission. Apply during DELIVER COMMIT, scheduled audits, or /nw-optimize-tests invocations.
1. Behavior Definition (the loose joint, closed)
The phrase "distinct behavior" in the test budget formula max_unit_tests = 2 × distinct_behaviors is the loose joint that lets test counts inflate. Close it with these rules.
1.1 What IS a behavior
A behavior is an observable outcome via a port (driving or driven):
- A return value from a driving-port call given specific inputs
- A state change visible through a driving-port query after an action
- A side effect at a driven-port boundary (call sequence + payload)
- An exception raised from a driving port
- A business invariant that holds across a class of inputs
1.2 What is NOT a behavior
- Phrase presence in markdown — that is one document contract, not one behavior per phrase
- AST shape (try/except wrapping, decorator presence, import order) — that is source structure, not runtime behavior
- Type system facts (subclass relations, attribute presence) — Python language guarantees, not domain behaviors
- Internal data shape (dataclass field assignment, dict structure) — language guarantees
- Internal method calls (mock.assert_called_with) — implementation, not outcome
- Source-shape compliance — runtime behavior on each supported environment IS the behavior, not the source it was compiled from
1.3 Counting rules — concrete cases
| Surface | Behaviors |
|---|---|
| Markdown skill with required phrases | 1 (the file conforms to the contract) |
| 5 skill files × 30 required phrases each | 5 (one per file), NOT 150 |
| Function with N input variations, same assertion shape | 1 (parametrize the variations) |
| Function with N error types, distinct messages and paths | N |
| Adapter that calls a driven port with ordered payload | 1 per call site, asserted once |
| Pure dataclass storing fields | 0 (Python guarantees this) |
| ABC with 5 abstract methods | 0 (Python guarantees abstract enforcement) |
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.
- 1mo ago First seen · 422 lines · 33 tokens per session scan A e7212a681c26
nw-test-optimization is a skill published in the GitHub repository nWave-ai/nWave (620 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 5,068 once invoked, about $0.0001 per session on Opus 5.5. 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.
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strict-tdd
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