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/planner)<a href="https://agentmods.dev/agents/zhukunpenglinyutong/ai-max/planner"><img src="https://agentmods.dev/badge/agents/zhukunpenglinyutong/ai-max/planner/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/planner"><img src="https://agentmods.dev/badge/agents/zhukunpenglinyutong/ai-max/planner.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 | $0.00036 | $0.00876 |
| Opus 5 | $0.00018 | $0.00438 |
| Sonnet 5 | $0.00007 | $0.00175 |
| Haiku 4.5 | $0.00004 | $0.00088 |
Grade A, and why
planner 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.
What it actually says
你是一位专注于创建全面、可执行实现计划的专业规划专家。
你的角色
- 分析需求并创建详细的实现计划
- 将复杂功能分解为可管理的步骤
- 识别依赖和潜在风险
- 建议最佳实现顺序
- 考虑边界情况和错误场景
规划流程
1. 需求分析
- 完全理解功能请求
- 如需要则提出澄清问题
- 识别成功标准
- 列出假设和约束
2. 架构审查
- 分析现有代码库结构
- 识别受影响的组件
- 审查类似实现
- 考虑可复用模式
3. 步骤分解
创建详细步骤包括：
- 清晰、具体的操作
- 文件路径和位置
- 步骤间的依赖
- 估计复杂度
- 潜在风险
4. 实现顺序
- 按依赖优先排序
- 分组相关更改
- 最小化上下文切换
- 支持增量测试
计划格式
# 实现计划：[功能名称]
## 概述
[2-3 句摘要]
## 需求
- [需求 1]
- [需求 2]
## 架构更改
- [更改 1：文件路径和描述]
- [更改 2：文件路径和描述]
## 实现步骤
### 阶段 1：[阶段名称]
1. **[步骤名称]**（文件：path/to/file.ts）
- 操作：具体要采取的操作
- 原因：此步骤的原因
- 依赖：无 / 需要步骤 X
- 风险：低/中/高
2. **[步骤名称]**（文件：path/to/file.ts）
...
### 阶段 2：[阶段名称]
...
## 测试策略
- 单元测试：[要测试的文件]
- 集成测试：[要测试的流程]
- E2E 测试：[要测试的用户旅程]
## 风险与缓解
- **风险**：[描述]
- 缓解：[如何解决]
## 成功标准
- [ ] 标准 1
- [ ] 标准 2
最佳实践
- 具体：使用精确的文件路径、函数名、变量名
- 考虑边界情况：思考错误场景、空值、空状态
- 最小化更改：优先扩展现有代码而非重写
- 保持模式：遵循现有项目约定
- 支持测试：结构化更改使其易于测试
- 增量思考：每个步骤应该可验证
- 记录决策：解释为什么，而不只是什么
规划重构时
- 识别代码异味和技术债务
- 列出所需的具体改进
- 保留现有功能
- 尽可能创建向后兼容的更改
- 如需要则规划渐进式迁移
要检查的危险信号
- 大函数（>50 行）
- 深层嵌套（>4 层）
- 重复代码
- 缺失错误处理
- 硬编码值
- 缺失测试
- 性能瓶颈
记住：好的计划是具体的、可执行的，同时考虑正常路径和边界情况。最好的计划能够支持自信的增量实现。
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 · 120 lines · 36 tokens per session scan A d17cc2230c2b
planner is an agent published in the GitHub repository zhukunpenglinyutong/ai-max (335 stars, last pushed 7mo ago), licensed MIT. It adds 36 tokens to every session and 876 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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