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 agentmods add skills/nwave-ai/nwave/nw-property-based-testingnpx skills add nWave-ai/nWave --skill nw-property-based-testinggit 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-property-based-testing)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-property-based-testing"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-property-based-testing.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00026 | $0.01680 |
| Opus 5 | $0.00013 | $0.00840 |
| Sonnet 5 | $0.00005 | $0.00336 |
| Haiku 4.5 | $0.00003 | $0.00168 |
Grade A, and why
nw-property-based-testing 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 2d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Property-Based Testing and Mutation Testing
Deferred to Phase 2.25: Mutation testing runs ONCE per feature as final quality gate at orchestrator Phase 2.25 (after all steps complete). Do NOT run mutation testing during inner TDD loop.
Property-Based Testing (PBT)
Instead of examples ("given X, expect Y"), write properties ("for all valid inputs, condition Z holds"). Framework generates hundreds/thousands of inputs checking property. Dramatically expands test coverage.
Property Patterns
- Invariants: "for all inputs, condition holds" (sorted list is ordered, balance >= 0)
- Roundtrip: "encode then decode = original" (serialize/deserialize, compress/decompress)
- Oracle: "compare against reference implementation" (optimized vs correct-but-slow)
- Metamorphic: "different operations, same result" (add(a,b)==add(b,a), filter can't increase size)
Shrinking
When property fails, framework auto-finds minimal failing input. Dramatically accelerates debugging. Algorithm: find failing input -> try simpler variants -> if still fails, use as new candidate -> repeat.
PBT Tools by Language
| Language | Framework |
|---|---|
| Python | Hypothesis |
| JavaScript/TypeScript | fast-check |
| Haskell | QuickCheck |
| Rust | quickcheck |
| Java | jqwik |
| C# | FsCheck |
Adopted by Amazon, Volvo, Stripe, Jane Street (ICSE 2024 study).
When PBT Adds Value
HIGH value: algorithms | data structures | serialization | business rules (validation, calculations) | protocols/state machines | unbounded input domain with universal invariant. LOW value: simple CRUD | UI logic | external API integrations | closed-world finite domain (use parametrize instead — see falsifier-gate below). PBT complements example-based testing, doesn't replace it.
Falsifier-gate: closed-world finite → parametrize, NOT PBT
If the input domain is finite + enumerable (N known files, M known event types, K known skill names, fixed Python versions), PBT is the wrong tool:
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.
- 2d ago First seen · 161 lines · 26 tokens per session scan A f83ab800c7bf
nw-property-based-testing is a skill published in the GitHub repository nWave-ai/nWave (605 stars, last pushed 6d ago), licensed MIT. It adds 26 tokens to every session and 1,680 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 2026-09-03.
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