ipd-tr

ipd-tr is a command for coding agents from hatewx/oh-my-ipd. It costs 41 tokens per session (1,196 once invoked), scanned A, original, MIT.

A command that runs a staged technical review process involving quality, testing, architecture, and requirements checks before a release decision. IPD-OMO is the process named in the add-on; its specialized role names are not explained further in the input.

In plain words
What is it for?
Use it to start a review, retry it after corrections, or inspect the current status, participating reviews, blockers, and past iterations.
Why use it?
It provides an ordered review path and sends failed checks back for fixes instead of treating release approval as a single unchecked step.

Command

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/hatewx/oh-my-ipd/ipd-tr
Clone the repo
git clone --depth 1 https://github.com/hatewx/oh-my-ipd

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 ipd-tr

README.md
[![agentmods](https://agentmods.dev/badge/commands/hatewx/oh-my-ipd/ipd-tr.svg)](https://agentmods.dev/commands/hatewx/oh-my-ipd/ipd-tr)
Your own site
<a href="https://agentmods.dev/commands/hatewx/oh-my-ipd/ipd-tr"><img src="https://agentmods.dev/badge/commands/hatewx/oh-my-ipd/ipd-tr.svg" alt="Measured on agentmods" height="20"></a>
Per session 41 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,196 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.

ModelPer sessionOnce invoked
Fable 5 $0.00041 $0.01196
Opus 5 $0.00020 $0.00598
Sonnet 5 $0.00008 $0.00239
Haiku 4.5 $0.00004 $0.00120

Measured 4d ago against content hash 9e3b6b9a7715, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ipd-tr 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 4d 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.

commands/ipd-tr.md · 143 lines

How it starts

The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TR Dry Run Loop 执行

TR Dry Run 是 IPD-OMO 的核心流程,负责质量门禁和持续改进。

流程图

Developer 提交
      │
      ▼
┌─────────────┐
│  PQA 检查   │──── Fail ────┐
│  (质量门禁) │              │
└──────┬──────┘              │
      │ Pass                │
      ▼                     │
┌─────────────┐             │
│  TMM 测试   │──── Fail ────┤
│  (功能验证) │              │
└──────┬──────┘              │
      │ Pass                │
      ▼                     │
┌─────────────┐             │
│  SE 评审    │──── Fail ────┤
│  (架构评审) │              │
└──────┬──────┘              │
      │ Pass                │
      ▼                     │
┌─────────────┐             │
│  PDU 验证   │──── Fail ────┘
│  (需求验证) │    (返回 Developer)
└──────┬──────┘
      │ Pass
      ▼
┌─────────────┐
│  LPDT 决策  │──── Pass ────▶ Release
│  (最终裁决) │
└─────────────┘──── Escalate ─▶ 用户决策

操作模式

action=start: 开始新的 TR

  1. 检查 Developer 是否提交了实现
  2. 按顺序激活 PQA → TMM → SE → PDU 进行评审
  3. 收集各角色的评审结果
  4. LPDT 综合评估并做出决策
  5. 更新项目状态

action=retry: 重新提交修复

  1. Developer 修复上一轮的问题
  2. 更新迭代计数
  3. 重新执行 TR 流程

action=status: 查看当前 TR 状态

  1. 显示当前 TR 进度
  2. 显示各角色评审状态
  3. 显示阻塞点和历史

执行细节

PQA 检查阶段

激活 pqa Agent,执行:

  • 流程合规检查
  • 代码规范检查 (Lint)
  • 质量门禁检查
  • 输出质量报告

TMM 测试阶段

激活 tmm Agent,执行:

  • 端到端测试
  • 边界条件测试
  • 错误处理测试
  • 输出测试报告

SE 评审阶段

激活 se Agent,执行:

  • 架构符合性检查
  • 设计模式检查
  • 技术债务检查
  • 输出架构评审报告

PDU 验证阶段

激活 pdu Agent,执行:

  • 需求符合度检查
  • 商业逻辑检查
  • 用户场景验证
  • 输出需求验证报告

LPDT 决策阶段

作为 LPDT,综合评估:

  • 所有评审结果
  • 迭代次数
  • Token 消耗
  • 风险等级

决策选项:

  • PASS: 进入下一阶段或 Release
  • FAIL: 返回 Developer 修复
  • ESCALATE: 请求用户介入

输出格式

TR 执行报告

╔════════════════════════════════════════════════════════════╗
║  TR Dry Run Report - Iteration [N]                         ║
╠════════════════════════════════════════════════════════════╣
║  提交时间: [timestamp]                                     ║
║  Developer: [状态]                                         ║
╠════════════════════════════════════════════════════════════╣
║  评审结果                                                  ║
║  ├─ PQA:  [✅/❌] [评分] [问题数]                          ║
║  ├─ TMM:  [✅/❌] [通过率] [缺陷数]                        ║
║  ├─ SE:   [✅/❌] [符合度] [问题数]                        ║
║  └─ PDU:  [✅/❌] [符合度] [偏离项]                        ║
╠════════════════════════════════════════════════════════════╣
║  LPDT 决策: [PASS/FAIL/ESCALATE]                           ║
║  决策理由: [理由]                                          ║
╠════════════════════════════════════════════════════════════╣
║  下一步                                                    ║
║  • [具体行动]                                              ║
╚════════════════════════════════════════════════════════════╝

Read the full file on GitHub · 143 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. 4d ago First seen · 143 lines · 41 tokens per session scan A 9e3b6b9a7715

Subscribe to this mod's changes

ipd-tr is a command published in the GitHub repository hatewx/oh-my-ipd (6 stars, last pushed 2mo ago), licensed MIT. It adds 41 tokens to every session and 1,196 once invoked, about $0.0002 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 2026-08-31.