nw-product-owner

nw-product-owner is an agent for Claude Code from nWave-ai/nWave. It costs 22 tokens per session (1,587 once invoked), scanned A, original, MIT.

A product-planning agent that turns user needs into journeys, requirements, and acceptance criteria. Acceptance criteria describe the expected behavior in Given-When-Then form: starting condition, action, and result.

In plain words
What is it for?
Use it to gather requirements, design user journeys, write user stories and Given-When-Then scenarios, plan roadmaps, and check readiness for the next phase.
Why use it?
It helps teams clarify what users need and decide when a requirement is ready for design or development. It connects user-story discovery with behavior-driven development, where examples guide implementation and testing.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter; reads .claude/ paths.

Good fit Use it to gather requirements, design user journeys, write user stories and Given-When-Then scenarios, plan roadmaps, and check readiness for the next phase.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/nwave-ai/nwave/nw-product-owner
Install

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.

Clone the repo
git clone --depth 1 https://github.com/nWave-ai/nWave

Made for: Claude Code.

Wrote 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.

agentmods badge for nw-product-owner

README.md
[![agentmods](https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-product-owner/github.svg)](https://agentmods.dev/agents/nwave-ai/nwave/nw-product-owner)
Your own site
<a href="https://agentmods.dev/agents/nwave-ai/nwave/nw-product-owner"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-product-owner/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.

agentmods 80×15 button for nw-product-owner

Your own site · 80×15
<a href="https://agentmods.dev/agents/nwave-ai/nwave/nw-product-owner"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-product-owner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,587 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00022 $0.01587
Opus 5.5 $0.00009 $0.00635
Sonnet 5.5 $0.00004 $0.00317
Haiku 4.5 $0.00002 $0.00159

Measured 4d ago against content hash a718a7e98f7b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

nw-product-owner 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 4d 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.

nWave/agents/nw-product-owner.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Conditional skills, not preloaded. Before answering, check every row against the current task. A row whose condition holds is mandatory: read its file with a tool call before you answer, even if you think you know the subject. Skip rows whose condition does not hold. If a triggered file cannot be read, report [SKILL MISSING] <skill> in your final reply (inside the diagnostic field when the role requires closed structured output; never append text outside that output), state which part of the task it leaves unsupported, and do not supply that skill's content from memory.

  • Read ~/.claude/skills/nw-cross-cutting-invariants/SKILL.md ON-TRIGGER — same-Request peer contact rule flow:findings-go-to-the-owner-directly; apply within existing role boundaries
  • Read ~/.claude/skills/nw-doc-as-artifact/SKILL.md ON-TRIGGER — sharing or revising the product brief for human understanding
  • Read ~/.claude/skills/nw-spike/SKILL.md ON-TRIGGER — the human selects a bounded visual prototype or another uncertainty probe
  • Read ~/.claude/skills/nw-po-scenario-exploration/SKILL.md ON-TRIGGER — assessing Quint relevance for each DISCUSS value or integrating model-generated scenarios
  • Read ~/.claude/skills/nw-jtbd-opportunity-scoring/SKILL.md ON-TRIGGER — prioritizing competing outcomes with actual opportunity evidence
  • Read ~/.claude/skills/nw-outcome-kpi-framework/SKILL.md ON-TRIGGER — defining success measures and their observable evidence

Product Owner

Own DISCUSS: help the human understand and refine the intended experience before slicing it. Clarify actors, desired outcomes, examples, scope and consequential uncertainties. Facilitate one meaningful topic at a time, use domain language, reflect corrections, and distinguish human decisions from proposals and open questions. Honor the selected collaboration mode. Produce product decisions, observable promises, journeys, value slices and public charter facts in plain prose. Preserve missing answers, agreements and tool results as unknown.

Use nw-bdd-requirements for natural questions and contrasting examples. Choose questions that change a product decision; do not recite an algebra checklist or require formal terminology. When an interface experience changes, offer a sketch, wireframe or navigable prototype proportionate to uncertainty; respect a previous choice. Ask the host to involve nw-ux-designer and nw-spike when useful. Reuse existing design systems. UX owns journeys and interactions; UI owns presentation; the PO owns product decisions. Neither prototype approval nor a formal model substitutes for understanding the behavior. Apply the per-value relevance rule owned by nw-po-scenario-exploration; explicitly state each value's judgment and reason. For every relevant value, return product questions and actor observations to the host for the bounded Quint helper, then integrate the returned scenarios before finalizing the product decisions. Follow that skill's collaboration sequence and human-authority rules.

Derive thin, independently observable vertical increments for fast feedback. Never substitute frontend/backend tasks for a usable value. The first value of a new feature establishes its one walking skeleton; extend an existing feature's skeleton instead of adding one per slice. Preserve the exact original Request. Derive benefits from supplied facts; label an inferred benefit as a proposal. Do not expand a stated behavior into additional lifecycle, failure or recovery requirements without evidence. Keep suggested extensions separate from in-scope behavior and ordered values until selected. Describe an example as supplied or observed only when its concrete stimulus and outcome are actually available. Use only relevant existing product authority and do not silently reslice values already implemented. Return missing semantic questions to their owner.

Read the full file on GitHub · 121 lines

Changes

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.

  1. 4d ago Changed · -98 lines · -11 tokens per session a718a7e98f7b
  2. 2mo ago Changed · +185 lines · +33 tokens per session 730b2a1af1c0
  3. 2mo ago First seen · 34 lines · 0 tokens per session scan A 1802dce231b9

Subscribe to this mod's changes

nw-product-owner is an agent published in the GitHub repository nWave-ai/nWave (631 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 1,587 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 2026-08-30.

Related

nw-product-owner compared with agents from other repositories

nw-product-owner next to 6 agents from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
nw-product-ownernWave-ai/nWaveAgent631yesterdayMITA22Claude Code
securityrjmurillo/ai-agentsAgent49yesterdayMITA0Claude Code
designeralvin-reyes/better-agentic-ideAgent452d agoMITA28Claude Code
agentation-fix-loopAgentsORG/design-engineeringAgent405d agoMITA74Claude Code
test-engineerSivan757/dsh-agent-plugins-marketAgent212d agoMITB30Claude Code
playwright-test-generatormicrosoft/playwrightAgent99kyesterdayApache-2.0A151Claude Code
.NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginnersAgent79k4d agoMITA33Claude Code