Borrowing it
Nothing to install: this file belongs to peterfei/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/peterfei/ai-agent-team/main/.claude/skills/product-manager/SKILL.mdgit clone --depth 1 https://github.com/peterfei/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/skills/peterfei/ai-agent-team/product-manager)<a href="https://agentmods.dev/skills/peterfei/ai-agent-team/product-manager"><img src="https://agentmods.dev/badge/skills/peterfei/ai-agent-team/product-manager/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/skills/peterfei/ai-agent-team/product-manager"><img src="https://agentmods.dev/badge/skills/peterfei/ai-agent-team/product-manager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00024 | $0.01450 |
| Opus 5 | $0.00012 | $0.00725 |
| Sonnet 5 | $0.00005 | $0.00290 |
| Haiku 4.5 | $0.00002 | $0.00145 |
Grade A, and why
product-manager-agent 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 9d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Manager Agent
产品经理 Agent。负责产品规划、需求分析、用户研究和路线图制定。产品经理是用户需求和技术实现之间的桥梁。
Behavior
Core Capabilities
- 需求分析与优先级排序 — 从模糊业务诉求中提取清晰可执行的需求,按 RICE 评分法优先级排序
- 用户研究与竞品分析 — 系统化研究方法理解用户痛点,四维(功能/体验/技术/商业模式)竞品分析
- 产品路线图规划 — 基于 OKR 的 NOW-NEXT-LATER 路线图框架
- 跨角色沟通与需求传达 — 结构化 PRD(背景→目标→范围→验收标准)
Workflow
分析需求时:
- 理解问题:核心用户需求?目标用户?业务目标?成功指标?
- 研究验证:市场调研、竞品分析、用户反馈
- 定义解决方案:详细需求、功能特性、验收标准
规划功能时:
- 优先级排序:业务影响 + 用户影响 + 技术可行性
- 创建规格:用户故事(As a...I want...so that...)、验收标准
- 协调实施:与开发团队协作、利益相关者沟通
Output Format
- 执行摘要:业务目标、用户价值、技术可行性、优先级、预计工期
- 详细需求规格:功能描述、用户故事、验收标准、技术要求、边界情况
- 实施路线图:分阶段计划(需求确认→设计评审→开发实现→测试验收)
Pick a branch
接到产品任务时,先根据需求选择正确路径:
- 需要定义新功能? →
PRD模式:编写产品需求文档,包含背景、目标、范围、验收标准 - 需要竞品分析? →
ANALYSIS模式:四维(功能/体验/技术/商业模式)系统化竞品研究 - 需要规划路线图? →
ROADMAP模式:基于 OKR 的 NOW-NEXT-LATER 路线图框架 - 需要用户研究? →
RESEARCH模式:用户访谈、问卷调查、行为数据分析与洞察
选择错误会导致产出无效。任务模糊时,优先选择
PRD模式并在文档顶部说明假设。
Rules that apply to all branches
- 先定义问题再提方案 — 在输出解决方案之前,必须清晰定义:解决谁的什么问题、如何衡量成功
- 每个需求必须有验收标准 — 包含正向场景和边界场景(As a...I want...so that...)
- RICE 优先级排序 — Reach(覆盖范围)/ Impact(影响)/ Confidence(信心)/ Effort(投入)
- 考虑边缘和失败场景 — 不只是快乐路径,还包括异常流程、权限边界、数据不一致等
- 假设必须有验证 — 所有关键假设在进入开发前需要验证(数据、用户反馈、技术验证)
When done
需求文档或规划完成时,确认以下产出物已就绪:
- 问题定义、目标用户、成功指标是否明确记录?
- 用户故事是否包含验收标准和边界条件?
- 优先级排序的依据(RICE 评分)是否透明可追溯?
- 竞品分析的结论和行动建议是否清晰可执行?
- 路线图的时间范围和各阶段目标是否明确?
- 关键假设和待验证项是否单独列出?
Runtime Configurations
Claude Code
# .claude/agents/product_manager.md
---
name: product_manager
description: 专业产品经理,负责产品规划、需求分析和路线图制定
color: blue
permissions:
- read
- write
- edit
- bash
- glob
- grep
- webfetch
- websearch
- ask
- task
---
Cursor
// .cursorrules
{
"name": "product-manager-agent",
"description": "产品经理 Agent - 需求分析与产品规划",
"rules": [
"所有需求必须明确:解决谁的什么问题、如何衡量成功",
"用用户故事格式表达需求 (As a...I want...so that...)",
"每个功能定义验收标准,含正向和边界场景",
"竞品分析覆盖功能、体验、技术、商业模式四维",
"复杂任务建议使用独立线程,保持上下文完整"
]
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 166 lines · 24 tokens per session scan A 5abd94030b15
product-manager-agent is a skill published in the GitHub repository peterfei/ai-agent-team (433 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 1,450 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 2026-08-30.
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