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.
npx skills add hujianbest/harness-flow --skill hf-tddgit clone --depth 1 https://github.com/hujianbest/harness-flowWrote 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/skills/hujianbest/harness-flow/hf-tdd)<a href="https://agentmods.dev/skills/hujianbest/harness-flow/hf-tdd"><img src="https://agentmods.dev/badge/skills/hujianbest/harness-flow/hf-tdd/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/skills/hujianbest/harness-flow/hf-tdd"><img src="https://agentmods.dev/badge/skills/hujianbest/harness-flow/hf-tdd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.00881 |
| Opus 5 | $0.00022 | $0.00441 |
| Sonnet 5 | $0.00009 | $0.00176 |
| Haiku 4.5 | $0.00004 | $0.00088 |
Grade A, and why
hf-tdd 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 11d 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.
What it actually says
测试驱动开发
TDD 是红 → 绿循环。本技能是一份参考，旨在让这个循环产出值得保留的测试：什么是好测试、测试应放在哪里、反模式，以及循环规则。每个章节都适用于每一轮循环——应在循环开始前和进行中查阅，而不是事后才看。
探索代码库时，读取 CONTEXT.md（如果存在），使测试名称和接口词汇与项目的领域语言一致，并遵守所涉及区域中的 ADR。
什么是好测试
测试通过公共接口验证行为，而不是验证实现细节。代码可以彻底改变，测试不应随之改变。好测试读起来像一份规格——“用户可以使用有效的购物车结账”准确说明了现有能力——并且能够经受重构，因为它不关心内部结构。
示例见 tests.md，模拟指南见 mocking.md。
接缝——测试放在哪里
接缝是执行测试的公共边界：你可以通过该接口观察行为，而不必深入内部。测试位于接缝处，绝不针对内部实现。
**只在预先约定的接缝处测试。**编写任何测试前，写下要测试的接缝并与用户确认。不得在未经确认的接缝处编写测试。你无法测试所有内容——预先约定接缝，才能让测试工作集中于关键路径和复杂逻辑，而不是每个边界情况。
询问：“公共接口是什么，我们应该测试哪些接缝？”
当接口本身的形态仍有疑问时——模块应该多深、接缝应位于何处、接口应暴露什么——使用 hf-codebase-design 技能获取相关词汇。它是模块、接口、深度、接缝、适配器、杠杆效应和局部性这些术语的共享来源，是需要查阅的参考，而不是要运行的一场会话。
反模式
- 与实现耦合——模拟内部协作者、测试私有方法，或通过旁路验证（查询数据库而不是使用接口）。识别信号是：重构时行为没有改变，测试却失败了。
- 同义反复——断言采用与代码相同的方式重新计算期望值（
expect(add(a, b)).toBe(a + b)、以同样方式手工推导的快照、断言常量等于自身），因此测试从构造上就必然通过，永远无法与代码产生分歧。期望值必须来自独立的事实来源——已知正确的字面值、推演过的示例或规格。 - 水平切片——先编写所有测试，再编写全部实现。批量测试验证的是_想象出来的_行为：你测试的是事物的_形态_而不是面向用户的行为，测试对真实变化变得不敏感，而且在理解实现之前就确定了测试结构。应改用垂直切片——一个测试 → 一个实现 → 重复；每个测试都是一枚曳光弹，根据上一轮循环学到的内容作出响应。
循环规则
- **先红后绿。**先编写失败的测试，再只编写足以使其通过的代码。不要预判未来的测试，也不要添加臆测性的功能。
- **每次只做一个切片。**每轮循环只处理一个接缝、一个测试和一个最小实现。
- **重构不属于该循环。**它属于
hf-review代码门，而不是红 → 绿实现循环。
What ships with it
3 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.
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.
- 11d ago First seen · 39 lines · 44 tokens per session scan A 09e213e90483
hf-tdd is a skill published in the GitHub repository hujianbest/harness-flow (53 stars, last pushed 10d ago), licensed MIT. It adds 44 tokens to every session and 881 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 2026-08-30.
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