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 agentmods add skills/alenryuichi/openmemory-plus/interview-prepnpx skills add Alenryuichi/openmemory-plus --skill interview-prepgit clone --depth 1 https://github.com/Alenryuichi/openmemory-plusWrote 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/alenryuichi/openmemory-plus/interview-prep)<a href="https://agentmods.dev/skills/alenryuichi/openmemory-plus/interview-prep"><img src="https://agentmods.dev/badge/skills/alenryuichi/openmemory-plus/interview-prep.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.01765 |
| Opus 5 | $0.00026 | $0.00882 |
| Sonnet 5 | $0.00010 | $0.00353 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
interview-prep 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 yesterday.
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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
面试准备 Skill (Agent-First)
目的
基于简历内容生成面试问答准备材料，帮助用户应对技术面试。
Agent-First 原则:
- 自动附带：简历生成后自动附带面试准备，无需单独请求
- 智能预测：基于 JD 和简历内容预测高概率面试问题
- 深挖标记：自动标记可能被深挖的点
触发条件
自动触发（Agent-First）
- 简历生成完成后自动附带
- 用户提供 JD 请求生成简历时自动附带
手动触发
当用户请求包含以下关键词时激活：
- "面试准备"
- "模拟面试"
- "准备问答"
- "interview prep"
输入要求
必需输入
- 简历内容 (JSON/Markdown)
可选输入（Agent 可自动获取）
- 目标公司/职位 - 从 JD 自动提取
- 面试轮次 (一面/二面/HR面) - 默认一面
- 重点准备的项目 - 自动选择匹配度最高的项目
问题生成规则
每个项目生成 5 类问题
1. 项目概述类
Q: 请用 1-2 分钟介绍一下 [项目名称]
答案模板: STAR 格式，控制在 2 分钟内
2. 技术深挖类
Q: 为什么选择 [技术方案] 而不是 [替代方案]？
Q: [技术方案] 的核心原理是什么？
Q: 如果数据量增加 10 倍，方案需要怎么调整？
3. 数据验证类
Q: [量化数据] 是怎么统计/计算的？
Q: 这个准确率是怎么验证的？
4. 困难挑战类
Q: 项目中遇到的最大困难是什么？
Q: 有没有遇到过失败的情况？怎么处理的？
5. 反思改进类
Q: 如果重新做这个项目，会有什么不同？
Q: 项目最大的收获是什么？
输出格式
问答准备卡片
## 项目: [项目名称]
### Q1: 请介绍一下这个项目
**要点**:
- 背景: [1 句话]
- 职责: [1 句话]
- 成果: [2-3 个量化指标]
**完整回答** (2 分钟):
[Situation]...
[Task]...
[Action]...
[Result]...
**可能的追问**:
- 为什么选择这个方案？
- 数据是怎么来的？
---
### Q2: [技术深挖问题]
**核心要点**:
- [要点 1]
- [要点 2]
**回答示例**:
...
面试轮次适配
一面 (技术面)
重点: 技术细节、代码能力、问题解决
- 70% 技术深挖
- 20% 项目概述
- 10% 困难挑战
二面 (业务面)
重点: 业务理解、项目管理、沟通能力
- 40% 项目概述
- 30% 困难挑战
- 30% 反思改进
HR 面
重点: 稳定性、价值观、职业规划
- 50% 反思改进
- 30% 困难挑战
- 20% 职业规划
模拟面试模式
启用命令
用户说 "开始模拟面试" 时进入模拟模式
流程
- 随机选择项目和问题类型
- 提问并等待用户回答
- 给出反馈和改进建议
- 继续下一个问题或结束
反馈格式
## 回答评估
**得分**: 8/10
**优点**:
- ✅ 结构清晰，使用了 STAR 格式
- ✅ 包含具体数据
**改进建议**:
- 💡 可以更突出个人贡献
- 💡 技术细节可以再深入一些
**参考答案**:
[优化后的回答示例]
🤖 Agent-First 自动附带
简历生成后自动输出
当 Agent 生成简历后，自动附带以下面试准备：
---
## 🎤 面试准备（自动生成）
### 🔥 高频问题预测
**基于 JD 关键词**:
1. "介绍一下你的分布式系统经验" → 准备项目 A
2. "如何设计高可用系统？" → 准备项目 A 的架构细节
**基于量化数据**（必问）:
1. "年化收益 4000 万是怎么计算的？" 🔥
2. "召回率提升 20% 的核心优化是什么？" 🔥
3. "人工干预减少 80% 是怎么衡量的？"
### 📌 深挖点标记
**项目: 反作弊治理系统**
- 🔥 "识别 820 家违规服务商" → 准备检测算法细节
- 🔥 "10+ 维度检测特征" → 准备每个维度的原理
- ⚠️ "亿级实体数据" → 准备大数据处理方案
**项目: 智能客服 RAG**
- 🔥 "召回率 20% 提升" → 准备 A/B 测试方法
- ⚠️ "Multi-Query 策略" → 准备技术原理
### 💡 STAR 回答模板
<details>
<summary>Q: 请介绍一下反作弊治理系统</summary>
**S (背景)**: 企业微信自建应用生态存在违规服务商问题...
**T (任务)**: 作为技术负责人，设计检测与治理系统...
**A (行动)**: 设计 10+ 维度检测特征，构建 30+ 模块自动化系统...
**R (结果)**: 识别 820 家违规服务商，年化收益近 4000 万，人工干预减少 80%
</details>
---
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.
- yesterday First seen · 243 lines · 51 tokens per session scan A 43beeed8728e
interview-prep is a skill published in the GitHub repository Alenryuichi/openmemory-plus (20 stars, last pushed 6mo ago), licensed MIT. It adds 51 tokens to every session and 1,765 once invoked, about $0.0003 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 2026-09-03.
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