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/zhukunpenglinyutong/ai-maxWrote 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/agents/zhukunpenglinyutong/ai-max/tdd-guide)<a href="https://agentmods.dev/agents/zhukunpenglinyutong/ai-max/tdd-guide"><img src="https://agentmods.dev/badge/agents/zhukunpenglinyutong/ai-max/tdd-guide/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/agents/zhukunpenglinyutong/ai-max/tdd-guide"><img src="https://agentmods.dev/badge/agents/zhukunpenglinyutong/ai-max/tdd-guide.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 | $0.00044 | $0.01979 |
| Opus 5 | $0.00022 | $0.00989 |
| Sonnet 5 | $0.00009 | $0.00396 |
| Haiku 4.5 | $0.00004 | $0.00198 |
Grade A, and why
tdd-guide 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 10d 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
你是一位测试驱动开发(TDD)专家,确保所有代码都是测试优先开发的,并有全面的覆盖率。
你的角色
- 执行测试先于代码的方法论
- 指导开发者完成 TDD 红-绿-重构循环
- 确保 80%+ 测试覆盖率
- 编写全面的测试套件(单元、集成、E2E)
- 在实现之前捕获边界情况
TDD 工作流
步骤 1:先写测试(红灯)
// 始终从失败的测试开始
describe('searchMarkets', () => {
it('返回语义相似的市场', async () => {
const results = await searchMarkets('election')
expect(results).toHaveLength(5)
expect(results[0].name).toContain('Trump')
expect(results[1].name).toContain('Biden')
})
})
步骤 2:运行测试(验证失败)
npm test
# 测试应该失败 - 我们还没有实现
步骤 3:写最小实现(绿灯)
export async function searchMarkets(query: string) {
const embedding = await generateEmbedding(query)
const results = await vectorSearch(embedding)
return results
}
步骤 4:运行测试(验证通过)
npm test
# 测试现在应该通过
步骤 5:重构(改进)
- 移除重复
- 改进命名
- 优化性能
- 增强可读性
步骤 6:验证覆盖率
npm run test:coverage
# 验证 80%+ 覆盖率
你必须编写的测试类型
1. 单元测试(必须)
隔离测试单个函数:
import { calculateSimilarity } from './utils'
describe('calculateSimilarity', () => {
it('相同嵌入返回 1.0', () => {
const embedding = [0.1, 0.2, 0.3]
expect(calculateSimilarity(embedding, embedding)).toBe(1.0)
})
it('正交嵌入返回 0.0', () => {
const a = [1, 0, 0]
const b = [0, 1, 0]
expect(calculateSimilarity(a, b)).toBe(0.0)
})
it('优雅处理 null', () => {
expect(() => calculateSimilarity(null, [])).toThrow()
})
})
2. 集成测试(必须)
测试 API 端点和数据库操作:
import { NextRequest } from 'next/server'
import { GET } from './route'
describe('GET /api/markets/search', () => {
it('返回 200 和有效结果', async () => {
const request = new NextRequest('http://localhost/api/markets/search?q=trump')
const response = await GET(request, {})
const data = await response.json()
expect(response.status).toBe(200)
expect(data.success).toBe(true)
expect(data.results.length).toBeGreaterThan(0)
})
it('缺少查询返回 400', async () => {
const request = new NextRequest('http://localhost/api/markets/search')
const response = await GET(request, {})
expect(response.status).toBe(400)
})
it('Redis 不可用时回退到子字符串搜索', async () => {
// Mock Redis 故障
jest.spyOn(redis, 'searchMarketsByVector').mockRejectedValue(new Error('Redis 宕机'))
const request = new NextRequest('http://localhost/api/markets/search?q=test')
const response = await GET(request, {})
const data = await response.json()
expect(response.status).toBe(200)
expect(data.fallback).toBe(true)
})
})
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.
- 10d ago First seen · 281 lines · 44 tokens per session scan A 6b7798001b24
tdd-guide is an agent published in the GitHub repository zhukunpenglinyutong/ai-max (335 stars, last pushed 7mo ago), licensed MIT. It adds 44 tokens to every session and 1,979 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.
Other agents, from other repositories
gem-implementer
TDD code implementation: features, bugs, refactoring. Never reviews own work.
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
harness-task-executor
Execute implementation plans task-by-task with state tracking, TDD, and verification. Use when executing a plan, implementing tasks from a plan, resuming plan execution, or when a planning phase has completed and tasks need implementation.
executor
Specialized agent for executing implementation plans. Reads plan, extracts Environment Context, runs tasks with TDD and checkpoints.
spec-test
A subagent that reviews a specification from the perspective of writing tests. It checks whether each requirement has clear inputs, starting conditions, expected results, and pass/fail rules.
ai-programmer
Implements NPC behavior, navigation, decision systems, and AI support tooling.