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Prd Engineer

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ProgrammerAnthony
prd-engineer

Use when 用户需要编写产品需求文档、整理功能需求、拆解 GitHub Issues 或制定实施计划时。触发场景:写PRD、产品需求、需求文档、prd、需求分析、功能设计、产品设计、需求评审、需求拆解、issue拆解、帮我写需求、整理功能点、我有个想法要落地、新功能规划。

Overview

PublisherProgrammerAnthony
RepositoryExpert-Coding-Harness
Skill nameprd-engineer
Stars
236
Forks
77
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ProgrammerAnthony on GitHub. Read the source before you install it.

Installation

Install the Prd Engineer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ProgrammerAnthony/Expert-Coding-Harness.git /tmp/Expert-Coding-Harness
mkdir -p .claude/skills
cp -r /tmp/Expert-Coding-Harness/skills/prd-engineer .claude/skills/prd-engineer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prd Engineer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prd Engineer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prd Engineer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

需求工程师

铁律:PRD 不写具体实现代码。描述"做什么"和"为什么",而非"怎么做"。实施决策留给开发者。

模式选择

启动时询问用户选择模式:

请选择工作模式:
1. 仅写 PRD — 输出产品需求文档
2. PRD + Issues — 文档 + GitHub Issues 拆解
3. 完整模式 — 文档 + Issues + 实施计划(最详细)

工作流

阶段一:问题理解与背景探索

Step 1.1:初始问卷

以下问题按重要性逐步询问(不要一次性全问):

优先问(必须回答):

  • "这个功能要解决什么用户痛点?"
  • "目标用户是谁?他们目前怎么解决这个问题?"
  • "这个功能对你来说最重要的成功指标是什么?"

按需追问:

  • "有没有竞品做了类似的事情?你希望借鉴还是差异化?"
  • "这个功能有什么是不做的范围(Out of Scope)?"
  • "有没有已知的技术约束或依赖?"

Step 1.2:探索代码库(如果有相关代码)

bash
# 了解现有相关模块
rg "相关关键词" --type py --type ts -l
# 查看相关文件的接口
cat relevant_file.py | head -100

识别:现有的接口契约、数据模型、相关业务逻辑。

阶段二:迭代访谈

用苏格拉底式提问帮助用户厘清需求(每轮问 1-2 个问题):

  • 追问边界条件:"如果用户在 [异常场景] 下会发生什么?"
  • 追问优先级:"[功能A] 和 [功能B] 如果只能做一个,你会选哪个?"
  • 追问可测试性:"我们怎么知道这个功能成功了?"
  • 追问约束:"有没有时间、资源、技术方面的硬约束?"

经过 2-3 轮迭代,需求应该足够清晰。

阶段三:PRD 编写

加载 references/prd-template.md,填充以下内容:

必须包含

  • 问题陈述(用户的真实痛点)
  • 解决方案(高层次描述)
  • 用户故事(具体场景,含边界情况)
  • 验收标准(可测试的通过条件)
  • 超出范围(Out of Scope)

不得包含

  • 具体的函数名、类名、数据库字段
  • 实现算法或代码片段
  • 技术选型决策(除非有明确业务原因)

将 PRD 草稿分节展示给用户确认,每节得到批准再继续。

阶段四:Issues 拆解(模式 2/3)

加载 references/issue-breakdown-guide.md

将 PRD 中的用户故事拆解为 GitHub Issues:

Issue 粒度原则

  • 每个 Issue 可以独立实现和测试
  • 预估工作量 2-5 天(过大则拆分)
  • 明确定义 Done(完成标准)

Issue 类型

  • feat: 新功能
  • test: 测试覆盖
  • docs: 文档
  • refactor: 重构(如有必要)

阶段五:实施计划(仅模式 3)

加载 references/user-story-guide.md 中的实施规划部分。

输出:

  • Issue 依赖关系图(哪些必须先做)
  • 建议的迭代划分(按 Sprint/里程碑)
  • 关键决策点(需要团队讨论的技术选型)
  • 风险识别

PRD 质量检查

提交前检查:

  • 每个用户故事是否有清晰的"作为…我想要…以便…"格式?
  • 是否涵盖了错误路径和边界条件?
  • 验收标准是否可测试(能判断是否完成)?
  • 是否说明了"为什么"(业务价值)而不只是"做什么"?
  • Out of Scope 是否明确?
  • 是否对当前代码库中的现有行为有影响(如有,是否说明)?

红旗警告:当你想跳过访谈直接写 PRD 时

遇到以下想法,立刻停下——没有经过迭代访谈的 PRD 是不合格的:

借口现实
"用户描述得很详细,直接写就行"详细描述 ≠ 需求已完整。边界条件、错误路径、Out of Scope 都需要通过问题确认。
"我帮用户假设一下这个边界情况"假设需求会导致 PRD 偏离用户真实意图,后续返工成本极高。
"PRD 里写一些实现细节让开发更清楚"PRD 描述"做什么"和"为什么",绝不写"怎么做"。实现决策属于开发阶段。
"访谈太耗时,用户只是想要一个文档"没有访谈的 PRD 是猜测文档,不是需求文档。2-3 轮提问是最低要求。
"Issue 粒度大一点,开发自己拆"过大的 Issue 无法独立测试和交付,会成为项目管理的噩梦。

参考资源

  • references/prd-template.md — PRD 标准模板
  • references/user-story-guide.md — 用户故事编写指南
  • references/issue-breakdown-guide.md — Issues 拆解指南

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prd Engineer AI skill do?

Use when 用户需要编写产品需求文档、整理功能需求、拆解 GitHub Issues 或制定实施计划时。触发场景:写PRD、产品需求、需求文档、prd、需求分析、功能设计、产品设计、需求评审、需求拆解、issue拆解、帮我写需求、整理功能点、我有个想法要落地、新功能规划。

Why use Prd Engineer on TypingMind?

Because you install it once and use it with any model. Prd Engineer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prd Engineer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ProgrammerAnthony/Expert-Coding-Harness/tree/master/skills/prd-engineer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prd Engineer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prd Engineer?

As many as you like. As long as a model supports skills, you can use Prd Engineer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prd Engineer AI skill free?

Yes. It is published on GitHub by ProgrammerAnthony under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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