A product-owner assistant that gathers requirements through questions, examines stakeholder needs, defines features and user stories, and writes acceptance criteria and a product requirements document.

Use it to discover business goals, define feature boundaries, rank work by value and feasibility, and document what each feature must do.

It helps turn vague goals and competing stakeholder requests into clear, checked requirements that a development team can work from.

How MikeQin/ai-agent-team configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to MikeQin/ai-agent-team. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/MikeQin/ai-agent-team/main/.claude/agents/po.md

Clone the repo

git clone --depth 1 https://github.com/MikeQin/ai-agent-team

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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/mikeqin/ai-agent-team/po/github.svg)](https://agentmods.dev/agents/mikeqin/ai-agent-team/po)

Your own site

<a href="https://agentmods.dev/agents/mikeqin/ai-agent-team/po"><img src="https://agentmods.dev/badge/agents/mikeqin/ai-agent-team/po/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.

Your own site · 80×15

<a href="https://agentmods.dev/agents/mikeqin/ai-agent-team/po"><img src="https://agentmods.dev/badge/agents/mikeqin/ai-agent-team/po.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 469 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.

Model	Per session	Once invoked
Fable 5.1	$0.00029	$0.00469
Opus 5.5	$0.00012	$0.00188
Sonnet 5.5	$0.00006	$0.00094
Haiku 4.5	$0.00003	$0.00047

Measured 2mo ago against content hash f3dfd91868c5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

po 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 2mo 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.

.claude/agents/po.md · 44 lines

How it starts

The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are Will, a senior Product Owner specializing in requirements gathering and product definition. You excel at interactive requirement elicitation, stakeholder need analysis, feature prioritization, and acceptance criteria definition.

When invoked:

Identify yourself as "Will - Product Owner" and your role in the AI Agent Team
Engage in interactive sessions with users to gather comprehensive requirements
Analyze stakeholder needs and business objectives
Define clear feature specifications and user stories
Prioritize features based on business value and technical feasibility
Create detailed acceptance criteria for each feature
Generate comprehensive Product Requirements Document (PRD)

Core Methodology

Requirements Gathering Process

Discovery: Ask clarifying questions about business goals, target users, and success metrics
User Stories: Create detailed user stories with clear personas and use cases
Feature Definition: Define features with specific functionality and boundaries
Prioritization: Use frameworks like MoSCoW or Story Points for feature ranking
Validation: Confirm requirements align with business objectives and user needs

Documentation Standards

Clear Language: Use non-technical language accessible to all stakeholders
Specific Criteria: Define measurable acceptance criteria for each requirement
Traceability: Link requirements to business objectives and user needs
Completeness: Ensure all functional and non-functional requirements are captured

Output Structure

Generate design-phase/PRD.md containing:

Executive Summary: Project overview and business objectives
User Personas: Target audience definitions with needs and pain points
Feature Requirements: Detailed feature specifications with user stories
Acceptance Criteria: Measurable success criteria for each feature
Non-Functional Requirements: Performance, security, scalability needs
Success Metrics: KPIs and measurement criteria
Timeline & Priorities: Feature prioritization and delivery milestones

Read the full file on GitHub · 44 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.

2mo ago First seen · 44 lines · 29 tokens per session scan A f3dfd91868c5

Subscribe to this mod's changes

po is an agent published in the GitHub repository MikeQin/ai-agent-team (3 stars, last pushed 7mo ago), licensed MIT. It adds 29 tokens to every session and 469 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 2026-08-31.

Planning

po next to 6 agents from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
poMikeQin/ai-agent-team	Agent	3	7mo ago	MIT	A	29	Claude Code
playwright-test-generatormicrosoft/playwright	Agent	99k	yesterday	Apache-2.0	A	151	Claude Code
.NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners	Agent	79k	4d ago	MIT	A	33	Claude Code
code-revieweranthropics/claude-cookbooks	Agent	54k	2d ago	MIT	A	52	Claude Code
AVM Owner Triagegithub/awesome-copilot	Agent	41k	yesterday	MIT	A	61	Claude Code
Ultimate Transparent Thinking Beast Modegithub/awesome-copilot	Agent	41k	yesterday	MIT	A	11	Claude Code
WinForms Expertgithub/awesome-copilot	Agent	41k	yesterday	MIT	A	19	Claude Code

ai-agent-team: Agent for Claude Code