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/dora0512/interview-trainer/review-skillnpx skills add Dora0512/interview-trainer --skill review-skillgit clone --depth 1 https://github.com/Dora0512/interview-trainerWrote 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/dora0512/interview-trainer/review-skill)<a href="https://agentmods.dev/skills/dora0512/interview-trainer/review-skill"><img src="https://agentmods.dev/badge/skills/dora0512/interview-trainer/review-skill.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 5.1 | $0.00045 | $0.01147 |
| Opus 5 | $0.00023 | $0.00574 |
| Sonnet 5 | $0.00009 | $0.00229 |
| Haiku 4.5 | $0.00005 | $0.00115 |
Grade A, and why
review-skill 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 5d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
复习技能点 Skill / One-Shot Review Card
当用户要复习某个面试技能点时,自动拉齐所有关联内容,生成一站式复习卡片。
语言规则:指令用中文写,但用用户的语言输出复习卡片。
使用方式
/review-skill <技能编号或名称>
例如:/review-skill 技能3、/review-skill <话题名>、/review-skill 协程。
工作区路径
| 内容 | 路径 |
|---|---|
| 技能定义 + 简历关联 | knowledge-base/topics.md |
| 用户画像(简历卖点 + 量化数据锁定) | profile.md |
| STAR 故事库 | knowledge-base/star-stories.md |
| Mermaid 图库 | knowledge-base/diagrams.md |
| 深度追问清单 | knowledge-base/deep-dive-questions.md |
| 深度技能指南(如有) | knowledge-base/guides/*.md |
| 类比银行 | knowledge-base/analogy-bank.md |
| 真实面试记录 | data/records/**/*.md |
| 能力画像 | data/capability-profile.md |
执行步骤
- 定位技能点:从
knowledge-base/topics.md找对应技能完整内容(掌握内容/能回答问题/达标判定/项目实践/简历关联)。 - 拉简历原文:从
profile.md指向的简历读「简历关联」对应条目原文。 - 拉 STAR 故事:从
star-stories.md读关联故事全文(S→T→A→R→追问准备)。 - 拉 Mermaid 图:从
diagrams.md读关联图代码,直接嵌入。 - 拉深度追问:从
deep-dive-questions.md找该技能对应追问列表。 - 拉技能深度指南(如有):从
knowledge-base/guides/*.md提取关键章节。 - 拉真实面试失分点(如有):从
data/records/**/*.md搜该技能相关记录,提取 哪些公司哪轮考过/当时水平/追问/答错点/发挥评估 ⚠️❌ 条目;从能力画像「真实面试验证记录」提取历史水平。
输出格式:一站式复习卡片
# 复习卡片:技能 X — [技能名称]
## 一句话类比
> [从 analogy-bank.md 取对应类比]
## 核心原理(含图)
[原理要点 + 嵌入 diagrams.md 的 Mermaid 图]
## 简历原文
> [简历对应段落完整引用] 量化数据:[profile.md 锁定的数字]
## STAR 故事
[完整 STAR 故事]
## 项目实战代码
> 模块:`<模块路径>` 核心类:`<类名>` 关键设计:[模式名称]
## 面试模拟
### 面试官可能问的问题
[从 deep-dive-questions.md 列 3-5 个追问]
### 每题参考回答结构
[类比开场 → 背景 → 原理 → 工程方案 → 数据结果 → 权衡]
## 真实面试复盘
> 你在真实面试中被考到该技能的表现记录:
| 公司 | 轮次 | 日期 | 达到水平 | 面试官追问 | 你的失分点 |
|------|------|------|---------|-----------|-----------|
**重点补强**:(基于真实失分点,给最需补强的 1-2 个知识点)
> 该技能未被真实面试考过时,显示"暂无真实面试记录,建议 `/interview mock` 模拟验证"。
## 自测清单
□ 能用类比解释核心概念(30秒)
□ 能画出关键流程图(1分钟)
□ 能讲出项目实战案例 + 量化数据(2分钟)
□ 能应对 3 个以上追问
□ 简历数字能脱口而出
□ 真实面试失分点已补强(如有)
关键规则
- 类比必须有:从 analogy-bank.md 取,没有则新建并标注
- 量化数字锁定:与
profile.md锁定数据一致 - Mermaid 图必须嵌入:是代码块,不是链接
- 追问至少 3 个
- 自测清单必须有
- 真实面试复盘必须有:被考过则展示失分点和补强建议
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.
- 5d ago First seen · 94 lines · 45 tokens per session scan A 43b2671a1f1b
review-skill is a skill published in the GitHub repository Dora0512/interview-trainer (10 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 1,147 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 2026-08-31.
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