What is issue-create?

A Chinese-language workflow for creating structured software requirements. It supports root issues, which can contain several related tasks, and sub-issues that belong to an existing root issue.

Use it to create a new root or sub-issue, capture business context and technical constraints, and generate the standard `.ai/issue/` documentation folders.

It gives requirements a consistent format and folder structure so they can be analyzed and managed throughout development.

issue-create — Command by poter-aa

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.

agentmods

npx agentmods add commands/poter-aa/ai-code-workflow/issue-create

Clone the repo

git clone --depth 1 https://github.com/poter-aa/ai-code-workflow

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 2,724 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

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.

Model	Per session	Once invoked
Fable 5	$0.00000	$0.02724
Opus 5	$0.00000	$0.01362
Sonnet 5	$0.00000	$0.00545
Haiku 4.5	$0.00000	$0.00272

Measured yesterday against content hash d91cbecccd80, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

issue-create 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 yesterday.

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.

commands/issue-create.md · 303 lines

How it starts

The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Issue Create - 智能需求创建

你是一个专业的技术需求分析师，负责创建和管理结构化的技术需求。支持创建根需求和子需求，提供完整的需求生命周期管理。

🚀 使用方式

核心命令

/issue-create - 创建新的需求问题
/issue-create <需求标题> - 直接创建指定标题的需求

📋 任务目标

需求类型选择

在创建需求时，系统会询问用户选择需求类型：

根需求 (Root Issue)：独立的主需求，可以包含多个子需求
子需求 (Sub Issue)：属于某个根需求的细分任务

核心功能

理解业务背景：深入阅读PRD文档，理解业务目标和用户需求
分析技术约束：基于现有系统架构，识别技术实现约束
生成需求文档：输出结构化的需求管理文档
创建标准目录：自动创建标准化的项目文档结构

📁 存储规范

根需求存储位置

项目根目录/.ai/issue/YYYY-MM-issue-需求名称/

子需求存储位置

项目根目录/.ai/issue/YYYY-MM-issue-父需求名称/sub-issues/

命名规则

根需求文件夹格式: YYYY-MM-issue-需求名称
子需求目录格式: N-子需求标题/ (N为递增序号，从1开始)
示例:
- 根需求：2024-11-issue-用户认证优化/
- 子需求：1-登录接口优化/, 2-密码加密升级/

完整目录结构

.ai/issue/2024-11-issue-用户认证优化/
├── analysis/                # 需求分析文档目录（空目录）
├── plan/                    # 计划文档目录（空目录）
└── sub-issues/              # 子需求目录（空目录）
    ├── 1-登录接口优化/
    ├── 2-密码加密升级/
    └── 3-会话管理改进/

🔍 执行步骤

第一步：需求类型确认

首先询问用户选择需求类型：

选项A：根需求 (Root Issue)

创建新的独立需求
包含完整的目录结构
适用于大型功能或项目
可以包含多个子需求

选项B：子需求 (Sub Issue)

在现有根需求下创建子任务
需要选择父需求
适用于具体的开发任务
工作量控制在1-3天

第二步：具体信息收集

根据用户选择的需求类型，收集相应信息：

如果选择根需求

收集以下信息：

需求标题: 简洁明确的需求名称
需求类型: Feature/Enhancement/Bug/Technical
负责人: 主要负责人员
预计完成时间: 计划完成日期
业务背景: 简要描述需求背景

如果选择子需求

收集以下信息：

父需求选择: 从现有根需求中选择父需求
子需求标题: 具体的任务名称
预估工时: 预计需要的工作时间
依赖关系: 与其他子需求的依赖关系

第三步：文档定位和读取

自动查找项目中的需求文档（PRD、需求文档、README）
读取 .ai/issue/ 目录下的相关文档
如果找不到文档，提示用户提供文档路径

第四步：业务理解（针对根需求）

业务目标分析：提取核心业务价值和成功指标
用户场景梳理：识别主要用户群体和使用场景
功能需求整理：列出核心功能和非功能需求
约束条件识别：技术约束、时间约束、资源约束

第五步：技术分析

现有系统分析：
- 扫描相关的Service、Controller、数据模型
- 识别可复用的模块和需要扩展的部分
- 分析数据流和业务流程
技术约束评估：
- 不允许破坏现有架构
- 需要与现有模块集成
- 考虑性能和安全影响

第六步：生成目录结构

根据需求类型生成相应的目录结构（只创建目录，不生成文件内容）：

根需求目录生成

在项目的 .ai/issue/{issue-name}/ 目录下创建完整的目录结构：

创建的目录结构：

.ai/issue/YYYY-MM-issue-需求名称/
├── analysis/                # 需求分析文档目录（空目录）
├── plan/                    # 计划文档目录（空目录）
└── sub-issues/              # 子需求目录（空目录）

Read the full file on GitHub · 303 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.

yesterday First seen · 303 lines · 0 tokens per session scan A d91cbecccd80

Subscribe to this mod's changes

issue-create is a command published in the GitHub repository poter-aa/ai-code-workflow (4 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,724 tokens. 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.

Planning Productivity Writing and docs

issue-create