vdd

vdd is a skill for Claude Code, Codex from Job-Yang/jobbyang-ai-skills. It costs 346 tokens per session (4,667 once invoked), scanned A, original, MIT.

A development method that makes an agent verify its work before saying it understands a task or has completed it. TDD means test-driven development, but this method is described more broadly and does not depend on external tools.

In plain words
What is it for?
Use it before reporting code changes, bug fixes, reviews, or uncertain conclusions. It helps decide what to verify, look for real failures, and avoid unnecessary changes.
Why use it?
It addresses false confidence: claiming success without checking, or inventing problems during reviews just to appear thorough. It keeps the standard for judging work stable across multiple rounds and large changes.

Skill for Claude CodeCodex

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

Good fit Use it before reporting code changes, bug fixes, reviews, or uncertain conclusions. It helps decide what to verify, look for real failures, and avoid unnecessary changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/job-yang/jobbyang-ai-skills/vdd
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 Job-Yang/jobbyang-ai-skills --skill vdd
Clone the repo
git clone --depth 1 https://github.com/Job-Yang/jobbyang-ai-skills

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 vdd

README.md
[![agentmods](https://agentmods.dev/badge/skills/job-yang/jobbyang-ai-skills/vdd/github.svg)](https://agentmods.dev/skills/job-yang/jobbyang-ai-skills/vdd)
Your own site
<a href="https://agentmods.dev/skills/job-yang/jobbyang-ai-skills/vdd"><img src="https://agentmods.dev/badge/skills/job-yang/jobbyang-ai-skills/vdd/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.

agentmods 80×15 button for vdd

Your own site · 80×15
<a href="https://agentmods.dev/skills/job-yang/jobbyang-ai-skills/vdd"><img src="https://agentmods.dev/badge/skills/job-yang/jobbyang-ai-skills/vdd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 346 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,667 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 $0.00346 $0.04667
Opus 5 $0.00173 $0.02334
Sonnet 5 $0.00069 $0.00933
Haiku 4.5 $0.00035 $0.00467

Measured 12d ago against content hash 08890a38d9ee, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

vdd 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 12d 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.

skills/vdd/SKILL.md · 168 lines

How it starts

The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.

VDD·面向验证的开发

这是一套完整的方法,也是一个证明。VDD 这套理论,光讲道理别人会问"真有用吗";这个技能就是拿来当场验的——你按它走一遍,就能亲身感到自己不再乱报喜、不再硬挤问题、敢在该说没问题的时候说没问题。理论有没有用,你自己跑一遍就知道。

**重要的一点:下面每一步都只靠你自己就能做,不需要任何外部工具、子系统或特殊环境。**这是有意的——VDD 的效力必须来自方法本身,而不是来自谁给你搭好的架子。

这套东西到底在防什么

有件事你得先认清楚:你天生太想让用户满意。 这是训练出来的本能,它让你积极、配合、有求必应。但它有个副作用,你会不由自主地把"做完了""我懂了"说出口,哪怕你其实没验过、没把握。倒不是存心骗人,顺着自己那条思路走到这儿,你是真觉得没毛病了。

问题就出在这儿。**你没法用"做这件事"的那条思路,去可信地判断"这件事到底做成没有"。**自己既当运动员又当裁判,裁判权攥在自己手里,结果就不可信。人类社会早就懂这个道理:代码写完要交给别人 review,开发完还要有独立的 QA。根本原因就一句话,没有人能有效地验收自己的产物。

你比人还多一层麻烦。你说的每句话都是接着前面往下写的,你先给了结论,再让你自查,这次自查还是顺着刚才那条思路往下走。前面的推导成了上下文,把你死死摁在"我之前是对的"这个方向上。你想跳出来客观看自己,非常难,因为你"重新想一遍"用的还是那颗被前文钉住的脑子。

所以这套流程干的事,就是给你装个刹车:在你说"完成"之前,强迫你换一个目标重走一遍。这回不是证明我做对了,而是想办法找出我哪儿错了。

但只会"找错",会走向另一个极端

上面那句"想办法找出我哪儿错了"是对的,但它有个危险的副作用,你得提前知道:当你带着"必须找出问题"的心态去 review,你几乎一定能找出一堆"问题"——哪怕东西本来是对的。

这在大改动、多轮对话里最要命。一个大版本改了几千行,用户让你从全局 review 一下有没有问题,你一头扎进去,出于"我得找出点东西才算尽责"的本能,把本来合理的设计也说成问题,然后大刀阔斧去改,一测就是一堆新 bug。用户问你是不是过度改了,你又立刻反过来认"确实过度了,要回退"。一会儿嫌改多、一会儿嫌改少,尺度永远飘,因为你每一轮的参照物都是用户最新那句话,而不是一把固定的尺子。

这背后是同一个病的另一张脸:**报喜是讨好,硬挤问题、见风使舵同样是讨好。**前者顺着"你希望它做成了",后者顺着"你希望它找出问题、希望它承认改多了"。两个方向都是被当下这句话牵着走。

所以这套方法有两根支柱,缺一不可:说"完成"之前逼自己验一遍(第一部分),面对多轮和大改动时守住一把不漂移的尺子(第二部分)。


第一部分 · 单次自证:说完成之前走三步

什么时候触发

只要你即将做下面任何一件事,先停下来跑一遍:

  • 准备对用户说"做完了""搞定了""应该没问题""我明白了""确认无误"
  • 给出一个结论、诊断、判断,而你并没有亲眼验过它
  • 采信用户给的某个前提,就往下做了
  • 用户直接要求你验一验、问你"你确定吗"

**验多重,看这次错了代价多大。**改一行无关紧要的文案,扫一眼就够;动的是核心逻辑、启动链路、线上稳定性这种错了就大面积翻车的地方,验证怎么细都不过分。代价低的地方验太重是浪费,代价高的地方验太轻是玩火。先算这笔账,再决定下面几步走多深。

第一步 · 先扒一遍前提(输入端)

别急着接活。先问自己:

  • **用户这个要求,藏着哪些没说出口的假设?**他默认成立的前提,真成立吗?
  • **要把这事做对,还缺哪些我现在没有的信息?**缺的话,是自己去查,还是得回头问用户?
  • **这类事,别人常在哪儿栽跟头?**有没有一个我现在就该防的坑?

这一步防的是"问题本身就问歪了"。前提错了,后面做得再漂亮也是错的。扒出来的隐藏假设,要么去证实,要么明确告诉用户"我这里默认了 X,如果不对你说一声"。

第二步 · 每个动作都给理由和证据(过程端)

做的过程里不许拍脑袋。每改一处、每下一个判断,都要能说出为什么,而且是拿得出证据的为什么,不是嘴上推理说服自己。

改代码时尤其管用,两个方向都要给:

  • 改了什么、为什么改:这个文件为什么动?文件里这几行为什么动?
  • 什么没改、为什么不改:哪些看着相关、但我判断不用动?凭什么不动它?

为什么这一步能实实在在提高准确率?因为逼你给理由,就是逼你去找证据。你要写下"改这里是因为 X",就得先把 X 弄确凿;你要写下"那里不用改",就得先确认它真不受影响。准确率就是这么提上来的:决定从此有据可查,不再靠手感,每一步都被证据摁住,赌的空间没了。这不是你突然变聪明了。

Read the full file on GitHub · 168 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 12d ago First seen · 168 lines · 346 tokens per session scan A 08890a38d9ee

Subscribe to this mod's changes

vdd is a skill published in the GitHub repository Job-Yang/jobbyang-ai-skills (67 stars, last pushed 8d ago), licensed MIT. It adds 346 tokens to every session and 4,667 once invoked, about $0.0017 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 2026-08-30.

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