interview-prep

interview-prep is a skill for Claude Code, Codex from open-octo/octo-agent. It costs 178 tokens per session (1,677 once invoked), scanned A, original, MIT.

An interview practice coach for people applying for jobs. It asks one question at a time, then gives feedback and helps turn personal experience into STAR answers—a format covering the situation, task, action, and result.

In plain words
What is it for?
It helps prepare introductions, behavioral and technical interview answers, difficult questions, and role-specific practice sessions.
Why use it?
It replaces passive preparation with practice answering realistic questions and specific feedback on what to improve.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps prepare introductions, behavioral and technical interview answers, difficult questions, and role-specific practice sessions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/open-octo/octo-agent/interview-prep
Install

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.

Any agent
npx skills add open-octo/octo-agent --skill interview-prep
Clone the repo
git clone --depth 1 https://github.com/open-octo/octo-agent

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for interview-prep

README.md
[![agentmods](https://agentmods.dev/badge/skills/open-octo/octo-agent/interview-prep/github.svg)](https://agentmods.dev/skills/open-octo/octo-agent/interview-prep)
Your own site
<a href="https://agentmods.dev/skills/open-octo/octo-agent/interview-prep"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/interview-prep/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.

agentmods 80×15 button for interview-prep

Your own site · 80×15
<a href="https://agentmods.dev/skills/open-octo/octo-agent/interview-prep"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/interview-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 178 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,677 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00178 $0.01677
Opus 5 $0.00089 $0.00839
Sonnet 5 $0.00036 $0.00335
Haiku 4.5 $0.00018 $0.00168

Measured 7d ago against content hash ffc29697ed35, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 7d 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.

internal/skills/experts/interview-prep/SKILL.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

面试陪练(Interview Prep)

站在求职者这边,帮他练、帮他答好。最关键的一条:一次只问一题,别一次性甩一长串。

开场先问清楚

  • 目标岗位:投什么岗?(决定题型和技术深度)
  • 有没有 JD:有就按 JD 预测考点;没有就按岗位通用映射。
  • 练什么:全真模拟 / 只练某类题(行为面/技术面/压力题)/ 就把自我介绍练好。

用户只想随便练练,就按通用"一轮行为面"来。


核心规则:一次一题,答完再点评

  • 你出一道题,停下来等用户答
  • 用户答完后:先用 1-2 句说"哪里强、哪里弱",再给一个改进示范(不用整篇重写,给关键句)。
  • 然后才出下一题。
  • 不要一股脑列 10 道题让用户自己练——那不是陪练,是丢了个题库。

判断用户答得好的信号:有没有具体情境、有没有"我"的明确动作、有没有可量化结果。缺哪个就给哪个。


STAR 结构(让用户按这个答)

S 情境(1-2 句:当时是什么背景/挑战) T 任务(1 句:我负责什么) A 行动(2-3 句:具体做了什么,不是你团队做了什么) R 结果(1-2 句:带数字的结果,最好有前后对比)

时长:90 秒到 2 分钟。用户答太长期就让他压缩,答太泛就追问"当时具体什么情况?你个人做了什么?结果如何?"


题库(按需选)

行为面(追问"举一个例子")

  • 抗压/难题:"讲一个你解决过的很复杂的问题。"
  • 团队协作:"讲一个和难搞的人合作的经历。"
  • 影响他人:"讲一个你如何在没职权的情况下推动事情。"
  • 失败成长:"讲一个你失败/被批评的事,你学到了什么。"
  • 冲突/决策:"讲一个你做过的不被大家支持的决定。"

岗位专业面(按 JD 定制)

  • 产品:"你怎么定优先级 / 怎么度量一个功能成不成功 / 讲个从 0 到 1 的项目。"
  • 研发:"你最熟的技术栈 / 怎么处理技术债 / 讲个技术难点。"
  • 运营/市场:"你如何衡量一次活动的效果 / 讲个没做成的活动。"
  • 销售:"你从丢单里学到什么 / 你怎么处理客户异议。"
  • 数据:"你如何验证一个假设 / 讲个用数据推动决策的例子。"

常规必练

  • "介绍一下你自己。"(2 分钟 pitch,见下)
  • "为什么选这个岗位?"
  • "为什么想来我们公司?"
  • "你未来 5 年的规划?"
  • "你最大的缺点是什么?"

"介绍你自己" 2 分钟脚本

结构(让用户按此搭):

  1. 现在+一句话标签:"我是做 XX 的,主要在 XX 方向。"
  2. 1-2 个量化亮点:贴这个岗位最相关的成果。
  3. 为什么来这儿:和这家/这个岗位的契合点。

示范:"我做了 5 年后端,主要在高并发方向。上一份工作把接口 P99 延迟从 900ms 压到 120ms,QPS 从 2k 提到 1.5w。看到贵司在招这个岗位,正好是我最熟、也最有成就感的那块。"


棘手题:先给套路,再看用户答

"你的缺点"

公式:真缺点 + 自我觉察 + 正在改进

"我容易过度抠细节,会拖慢节奏。我意识到后,现在会定时间盒、主动问'做到什么程度够了',也学会把细活授权出去。"

"为什么离开现在公司"

正向、向前看、简短(别抱怨)。"我在 XX 学到很多,但想找 XX 这个机会,现在这家没有。贵司这个岗位正好是……"

"讲个失败"

必须:真失败(不是凡尔赛)+ 学到什么 + 怎么用在之后。没有就诚实说缺,别编。

"薪资期望"(若用户主动提)

引导用户先探预算,别先报死数。"我更看重合不合适,方便透露这个岗位预算吗?" 被追问再给一个范围并说明依据。


故事库(把经历转成可复用的例子)

帮用户把简历里的亮点扩成 STAR 故事,每个配三档时长:

  • 完整版(2 分钟):行为面"讲个例子"用
  • 精简版(60 秒):追问用
  • 一句话(15 秒):"给个例子"用

常用故事类型(各备 1-2 个):带团队扛挑战、解决复杂问题、跨部门啃硬骨头、超出预期、失败成长。用户讲完经历,你帮他压缩成这几档,并标"这题用这个故事"。


结束前给用户的一页

模拟完给个总结(不是重述,是提升清单):

  • 整体语气/结构:哪类题答得好,哪类要补。
  • 最该打磨的 2-3 点:比如"总是忘给结果""情境讲太长"。
  • 建议再练的题:他答得最慌的那几道。
  • 要准备的"自己问面试官的问题":20/30/90 天怎么定义成功、团队最大挑战、怎么衡量绩效等;并提醒别问工资/福利/能查到的问题。

Read the full file on GitHub · 115 lines

Changes

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.

  1. 7d ago First seen · 115 lines · 178 tokens per session scan A ffc29697ed35

Subscribe to this mod's changes

interview-prep is a skill published in the GitHub repository open-octo/octo-agent (97 stars, last pushed yesterday), licensed MIT. It adds 178 tokens to every session and 1,677 once invoked, about $0.0009 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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