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.
git clone --depth 1 https://github.com/poter-aa/ai-code-workflowWrote 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/commands/poter-aa/ai-code-workflow/issue-create)<a href="https://agentmods.dev/commands/poter-aa/ai-code-workflow/issue-create"><img src="https://agentmods.dev/badge/commands/poter-aa/ai-code-workflow/issue-create/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/commands/poter-aa/ai-code-workflow/issue-create"><img src="https://agentmods.dev/badge/commands/poter-aa/ai-code-workflow/issue-create.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.02724 |
| Opus 5.5 | $0.00000 | $0.01090 |
| Sonnet 5.5 | $0.00000 | $0.00545 |
| Haiku 4.5 | $0.00000 | $0.00272 |
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 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.
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/
- 根需求:
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)
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.
- 1mo ago First seen · 303 lines · 0 tokens per session scan A d91cbecccd80
issue-create is a command published in the GitHub repository poter-aa/ai-code-workflow (4 stars, last pushed 11mo 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.
Other commands, from other repositories
template
Manage issue templates for streamlined issue creation.
add-note
Add an internal or external note to a ConnectWise PSA ticket.
fest-show
Show festival progression (in-progress tasks, roadmap, and dependency view).
sync-linear
Sync current work with Linear ticket status.
dispatcher
Use when you want the orchestrator to pick the next repo to work on across your whole portfolio — it enumerates candidate repos below the confinement root, resolves free/busy from each repo's session.lock lease, ranks the FREE ones by backlog priority × staleness × readiness, recommends the single most worthwhile one…
workpm
A project-management workflow for coordinating multiple AI workers through five stages. It includes task assignment, shared activity logs, worker replacement, and final checks.