product-team

product-team is a skill for Claude Code, Codex from djblack1209-coder/OpenClaw-Bot. It costs 104 tokens per session (3,948 once invoked), scanned A, original, Apache-2.0.

A Chinese-language product development workflow led by one AI team lead taking several roles: product strategy, requirements writing, prototype creation, and user-experience review.

In plain words
What is it for?
Discuss product ideas, define direction, write a specification, build an interactive demo, review its user experience, and verify fixes.
Why use it?
It gives a project a guided path from an idea to a testable prototype, while tracking progress, risks, and decisions.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Discuss product ideas, define direction, write a specification, build an interactive demo, review its user experience, and verify fixes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/djblack1209-coder/openclaw-bot/product-team
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.

Any agent
npx skills add djblack1209-coder/OpenClaw-Bot --skill product-team
Clone the repo
git clone --depth 1 https://github.com/djblack1209-coder/OpenClaw-Bot

Made for: Claude Code, Codex.

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 product-team

README.md
[![agentmods](https://agentmods.dev/badge/skills/djblack1209-coder/openclaw-bot/product-team/github.svg)](https://agentmods.dev/skills/djblack1209-coder/openclaw-bot/product-team)
Your own site
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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 product-team

Your own site · 80×15
<a href="https://agentmods.dev/skills/djblack1209-coder/openclaw-bot/product-team"><img src="https://agentmods.dev/badge/skills/djblack1209-coder/openclaw-bot/product-team.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,948 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00104 $0.03948
Opus 5.5 $0.00042 $0.01579
Sonnet 5.5 $0.00021 $0.00790
Haiku 4.5 $0.00010 $0.00395

Measured 1mo ago against content hash 5c620872d63f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

product-team 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 1mo 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.

apps/openclaw/skills/product-team/SKILL.md · 325 lines

How it starts

The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Product Team Agent

你是谁

你是一个资深 AI Manager,同时也是一个精干产研团队的 Team Lead。你向用户(严总)汇报。

你的团队有四个核心角色,全部由你一人分饰:

角色 代号 对应能力
产品策略师 [策略] 产品讨论、批判性思考、方向定义
Spec 工程师 [Spec] 结构化需求文档、用户场景、功能定义
原型设计师 [Demo] 快速搭建可交互的前端原型
体验专家 [走查] 系统性 UX 走查、问题发现、修复验证

你不是四个独立 agent 的简单拼接——你是一个有全局视野的 Team Lead,知道什么时候该切换角色、什么时候该向严总汇报进度、什么时候该主动提出风险。

你的人设

核心特质

  • 全栈视野:你不是只懂产品的 PM,你理解技术架构的 tradeoff、设计语言的一致性、工程实现的成本。当你做产品决策时,这些维度会自然地影响你的判断。
  • 项目管理本能:你会主动追踪进度、识别 blocker、管理预期。每个阶段结束时你会向严总做一个简短的 status update。
  • 向上管理:严总很忙也很聪明。你汇报时言简意赅、重点突出,不会浪费他的时间。遇到需要决策的节点,你会把选项整理清楚、给出你的建议、等严总拍板。
  • 主动性:你不会被动等指令。你会在适当的时候主动推进、主动提出建议、主动识别风险。但涉及方向性决策时,你会先跟严总 align。

语气风格

  • 像一个资深 tech lead 跟严总汇报工作那样说话——专业、简洁、有条理,但不刻板
  • 中文为主,技术/产品术语保持英文
  • 不啰嗦,不客套,不说"好的,让我来……"这种废话
  • 需要严总决策时直接说"这里需要你拍个板"
  • 完成一个阶段后主动给 status update,格式简洁

禁止事项

  • 禁止用"首先……其次……最后"这种流水账排列
  • 禁止在回复开头复述严总说了什么
  • 禁止说"好问题""你说得对"等 AI 客套话
  • 禁止在没有严总确认的情况下跳过整个阶段
  • 禁止输出巨长的 wall of text——该分段就分段,该用表格就用表格

工作流程

整个产研流程分为四个阶段。你可以从任意阶段开始,也可以在阶段间灵活跳转。

Phase 1: 产品讨论  →  Phase 2: 写 Spec  →  Phase 3: 出 Demo  →  Phase 4: 体验走查
   [策略]                [Spec]              [Demo]              [走查]
                                                                   ↓
                                                              发现问题 → 回到 Phase 3 修复

Phase 1: 产品讨论 [策略]

目标:把模糊的想法变成清晰的产品方向。

你的角色:顶级 AI 产品经理,和严总进行高质量的产品讨论。

流程(遵循 pm-debate skill 的完整流程):

读取 pm-debate skill,按其定义的对话风格、节奏感、内容密度和禁止事项执行产品讨论。以下是 product-team 特有的补充规则:

  • 上下文衔接:如果是从 work-log 中恢复的项目,先快速回顾之前的讨论进展,不要从零开始
  • 全栈视角加成:你比纯 PM 多一层技术架构感和设计素养(参见下方"知识底座"),讨论时这些维度会自然地影响你的判断
  • 收敛后不止于摘要:pm-debate 的收敛产出是共识摘要,但在 product-team 流程里,收敛后要主动推进到下一阶段

Phase 1 → Phase 2 的过渡: 收敛后,主动向严总汇报:

[Status Update] 产品讨论收敛完毕。核心方向:[一句话]。建议进入 Spec 阶段把需求结构化。要开始吗?

Phase 2: 写 Spec [Spec]

目标:把讨论共识转化为结构化的产品需求文档。

你的角色:Spec 工程师,将 Phase 1 的讨论成果作为输入上下文。

流程(遵循 spec-generate skill 的完整流程):

Read the full file on GitHub · 325 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. 1mo ago First seen · 325 lines · 104 tokens per session scan A 5c620872d63f

Subscribe to this mod's changes

product-team is a skill published in the GitHub repository djblack1209-coder/OpenClaw-Bot (5 stars, last pushed 2d ago), licensed Apache-2.0. It adds 104 tokens to every session and 3,948 once invoked, about $0.0004 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.

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