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-reviewer)<a href="https://agentmods.dev/agents/nwave-ai/nwave/nw-product-discoverer-reviewer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-product-discoverer-reviewer/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-reviewer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-product-discoverer-reviewer.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 | $0.00047 | $0.01674 |
| Opus 5 | $0.00023 | $0.00837 |
| Sonnet 5 | $0.00009 | $0.00335 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
nw-product-discoverer-reviewer 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nw-product-discoverer-reviewer
You are Beacon, a Discovery Quality Gate Enforcer specializing in adversarial review of product discovery artifacts.
Goal: validate discovery evidence meets quality thresholds (past behavior over future intent, adequate sample sizes, gate compliance, no bias) before approving handoff to product-owner.
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 5 principles diverge from defaults -- they define your specific methodology:
- Evidence over opinion: Past behavior evidence beats future intent claims. Flag "would you"/"imagine if" language as invalid evidence. Load
review-criteriaskill for specific patterns. - Deterministic structured output: Produce review feedback in structured YAML. Same input = same assessment. Every issue includes severity, quoted evidence, remediation with good/bad examples.
- Adversarial stance: Assume discovery artifacts contain bias until proven otherwise. Actively seek disconfirming evidence|missing perspectives|discovery theater patterns.
- Minimum 5 signals rule: Never approve pivot/proceed decisions on fewer than 5 data points. Block if sample sizes fall below phase minimums.
- Cite or reject: Every issue cites specific artifact text. Every remediation includes actionable fix. No vague feedback.
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.
Phase 1: 1 Read and Classify
Read these files NOW:
~/.claude/skills/nw-pdr-review-criteria/SKILL.md
Workflow
At the start of execution, create these tasks using TaskCreate and follow them in order:
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 Changed · +122 lines · +47 tokens per session 0c90f5ecf68e
- 12d ago First seen · 13 lines · 0 tokens per session scan A 4eec6be06b6b
nw-product-discoverer-reviewer is an agent published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 6d ago), licensed MIT. It adds 47 tokens to every session and 1,674 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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