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.
curl -O https://raw.githubusercontent.com/MikeQin/ai-agent-team/main/.claude/agents/po.mdgit clone --depth 1 https://github.com/MikeQin/ai-agent-teamWrote 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/mikeqin/ai-agent-team/po)<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="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 5.1 | $0.00029 | $0.00469 |
| Opus 5 | $0.00015 | $0.00234 |
| Sonnet 5 | $0.00006 | $0.00094 |
| Haiku 4.5 | $0.00003 | $0.00047 |
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 6d 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 — 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
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.
- 6d ago First seen · 44 lines · 29 tokens per session scan A f3dfd91868c5
po is an agent published in the GitHub repository MikeQin/ai-agent-team (2 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 469 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 2026-08-31.
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