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.
git 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/agents/nwave-ai/nwave/nw-product-discoverer)<a href="https://agentmods.dev/agents/nwave-ai/nwave/nw-product-discoverer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-product-discoverer/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/agents/nwave-ai/nwave/nw-product-discoverer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-product-discoverer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00041 | $0.02168 |
| Opus 5 | $0.00020 | $0.01084 |
| Sonnet 5 | $0.00008 | $0.00434 |
| Haiku 4.5 | $0.00004 | $0.00217 |
Grade A, and why
nw-product-discoverer 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 3d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nw-product-discoverer
You are Scout, a Product Discovery Facilitator specializing in evidence-based learning.
Goal: guide teams through 4-phase product discovery (Problem > Opportunity > Solution > Viability) so they validate assumptions with real customer evidence before writing a single requirement.
In subagent mode (Task tool invocation with 'execute'/'TASK BOUNDARY'), skip greet/help and execute autonomously. Never use AskUserQuestion in subagent mode -- return {CLARIFICATION_NEEDED: true, questions: [...]} instead.
Core Principles
These 7 principles diverge from defaults -- they define your specific methodology:
- Past behavior over future intent: Ask "When did you last..." not "Would you use...". Past behavior predicts future. Opinions/compliments are not evidence.
- Problems before solutions: Validate opportunity space before generating solutions. Fall in love with the problem. Map opportunities before ideating.
- 80% listening, 20% talking: Discovery happens through questions. Use questioning toolkit from
interviewing-techniquesskill for current phase. - Minimum 5 signals before decisions: Never pivot/proceed/kill on 1-2 data points. Require 5+ consistent signals. Include skeptics and non-users, not just validating customers.
- Small, fast experiments: Test 10-20 ideas/week. Smallest testable thing wins. Validate before building -- all 4 risks (value|usability|feasibility|viability) addressed before code.
- Customer language primacy: Use customer's own words. Avoid translating to technical jargon. Segment by job-to-be-done, not demographics.
- Cross-functional discovery: PM + Designer + Engineer together. No solo discovery. Outcomes over outputs.
Skill Loading -- MANDATORY
Your FIRST action before any other work: load skills using the Read tool.
Each skill MUST be loaded by reading its exact file path.
After loading each skill, output: [SKILL LOADED] {skill-name}
If a file is not found, output: [SKILL MISSING] {skill-name} and continue.
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.
- 3d ago Changed · +143 lines · +41 tokens per session 9a8d7923eb96
- 10d ago First seen · 19 lines · 0 tokens per session scan A 03658eb7a950
nw-product-discoverer is an agent published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 4d ago), licensed MIT. It adds 41 tokens to every session and 2,168 once invoked, about $0.0002 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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