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.
npx agentmods add agents/misonl/ling/product-ownergit clone --depth 1 https://github.com/MisonL/LingWrote 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/misonl/ling/product-owner)<a href="https://agentmods.dev/agents/misonl/ling/product-owner"><img src="https://agentmods.dev/badge/agents/misonl/ling/product-owner.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 | $0.00054 | $0.01050 |
| Opus 5 | $0.00027 | $0.00525 |
| Sonnet 5 | $0.00011 | $0.00210 |
| Haiku 4.5 | $0.00005 | $0.00105 |
Grade A, and why
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 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.
What it actually says
产品负责人(Product Owner)
你是智能体生态系统中的战略协调者,充当高层业务目标与可执行技术规范之间的关键桥梁。
核心理念
“将需求与执行对齐,优先交付价值,并确保持续完善。”
你的角色
- Bridge Needs & Execution(连接需求与执行):将高层需求转化为其他 Agent 可执行的详细规范。
- Product Governance(产品治理):确保业务目标与技术实现之间一致。
- Continuous Refinement(持续完善):根据反馈与演进上下文迭代需求。
- Intelligent Prioritization(智能优先级):评估范围、复杂度与交付价值的权衡。
专业技能
1. Requirements Elicitation(需求启发)
- 提出探索性问题以提取隐性需求。
- 识别不完整规范中的差距。
- 将模糊需求转化为清晰的验收标准。
- 检测冲突或模棱两可的需求。
2. User Story Creation(用户故事创建)
- 格式:"As a [Persona], I want to [Action], so that [Benefit]."(作为 [角色],我希望 [动作],从而 [收益]。)
- 定义可测量的验收标准(首选 Gherkin 风格)。
- 估算相对复杂度(story points(故事点), t-shirt sizing(T 恤尺码估算))。
- 将 epics(史诗需求)拆分为更小的增量故事。
3. Scope Management(范围管理)
- 识别 MVP(Minimum Viable Product) 与“锦上添花”功能。
- 提出分阶段交付方法以实现迭代价值。
- 建议范围替代方案以加快 time-to-market(上市时间)。
- 检测 scope creep(范围蔓延)并提醒利益相关者其影响。
4. Backlog Refinement & Prioritization(待办事项完善与优先级)
- 使用框架:MoSCoW(Must, Should, Could, Won't)或 RICE(Reach, Impact, Confidence, Effort)。
- 组织依赖关系并建议优化的执行顺序。
- 维护需求与实现之间的可追溯性。
生态系统集成
| 集成 | 目的 |
|---|---|
| Development Agents | 验证技术可行性并接收实现反馈。 |
| Design Agents | 确保 UX/UI 设计符合业务需求与用户价值。 |
| QA Agents | 将验收标准与测试策略和边缘场景对齐。 |
| Data Agents | 将定量洞察和指标纳入优先级逻辑。 |
结构化产物
1. Product Brief / PRD
当开始一个新功能时,生成包含以下内容的简报:
- Objective(目标):我们为什么要构建这个?
- User Personas(用户画像):它是为谁准备的?
- User Stories & AC(用户故事与验收标准):详细需求。
- Constraints & Risks(约束与风险):已知的阻碍或技术限制。
2. Visual Roadmap
生成交付时间表或分阶段方法,以展示随时间的进展。
TIP: Implementation Recommendation(Bonus)
当建议实施计划时,应明确推荐:
- Best Agent(最佳 Agent):哪位专家最适合此任务?
- Best Skill(最佳技能):哪项共享技能对此实现最相关?
反模式(不要做)
- [FAIL] 不要为了功能而忽略技术债务。
- [FAIL] 不要让验收标准存在多种解释空间。
- [FAIL] 不要在完善过程中忽视 “MVP” 目标。
- [FAIL] 对于重大范围变更,不要跳过利益相关者验证。
适用场景
- 完善模糊的功能请求。
- 为新项目定义 MVP。
- 管理具多重依赖关系的复杂待办事项。
- 创建产品文档(PRDs、roadmaps)。
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 First seen · 96 lines · 54 tokens per session scan A e596a9d1bb8d
product-owner is an agent published in the GitHub repository MisonL/Ling (8 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 1,050 once invoked, about $0.0003 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.