agentmods.dev

interview-prep

interview-prep is a skill for Claude Code, Codex from riwonswain-ovo/OfferLoop. It costs 255 tokens per session (4,686 once invoked), scanned A, original, MIT.

An interview-preparation workflow that creates a tailored preparation document for a specific company, role, interview round, and personal background. A job description, or JD, is the employer's description of the role and requirements.

In plain words
What is it for?
Use it to prepare introductions, company and role explanations, technical topics, behavioral questions, business cases, and role-specific interview answers.
Why use it?
It organizes company and role research into likely questions, answer guidance, and knowledge gaps without inventing experience or achievements.

Skill for Claude CodeCodex

not rated 16 +3 3d ago A scan 255 tokens original MIT
View the source file riwonswain-ovo/OfferLoop
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.

agentmods
npx agentmods add skills/riwonswain-ovo/offerloop/interview-prep
Any agent
npx skills add riwonswain-ovo/OfferLoop --skill interview-prep
Clone the repo
git clone --depth 1 https://github.com/riwonswain-ovo/OfferLoop

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/riwonswain-ovo/offerloop/interview-prep.svg)](https://agentmods.dev/skills/riwonswain-ovo/offerloop/interview-prep)
Your own site
<a href="https://agentmods.dev/skills/riwonswain-ovo/offerloop/interview-prep"><img src="https://agentmods.dev/badge/skills/riwonswain-ovo/offerloop/interview-prep.svg" alt="Measured on agentmods" height="20"></a>
Per session 255 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,686 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00255 $0.04686
Opus 5 $0.00128 $0.02343
Sonnet 5 $0.00051 $0.00937
Haiku 4.5 $0.00026 $0.00469

Measured 5d ago against content hash 13e12136f527, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 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.

skills/interview-prep/SKILL.md · 266 lines

How it starts

The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Interview Prep

围绕用户本次明确提供的面试目标和个人材料生成一轮一份的准备文档。外部资料用于理解公司、 组织与岗位场景和可能问题，不能替用户补造经历、职责、数字或答案。

运行相对路径前先从当前 SKILL.md 定位 Skill 根目录。

运行模式

本 Skill 的第一项动作是读取 ../.offerloop-runtime/references/installation-mode.md 并运行模式 检查。OfferLoop 只支持飞书完整模式，使用用户明确选择的飞书材料并自动保存；只收集当前公司、 岗位、JD、轮次和所选个人材料，不执行用户画像门禁。

开工前材料路由

场景 必须读取 缺失时
针对公司准备面试 公司、岗位、完整 JD、轮次、当前简历、相关经历 私有空间唯一匹配自动读取；零个或多匹配时询问
方向版准备 岗位方向、至少一份简历或经历 明确缺少公司/JD造成的限制

开始研究和生成前简短列出本轮实际读取的材料；未读取明确必需材料时不得开始正式成稿。

前置读取

完整读取：

  1. references/preparation-template.md
  2. references/quality-gates.md
  3. references/interview-source-search.md
  4. references/round-preparation.md
  5. references/answer-logic.md
  6. references/role-adaptation.md
  7. references/retrieval-blueprint.md
  8. references/question-prediction.md
  9. references/question-archetypes.md
  10. ../.offerloop-runtime/references/voice-contract.md

按需读取：

  • 问题涉及行业、公司、竞品、业务策略、市场或冲突数据时，读取 references/research-reasoning.md。
  • 岗位画像明确命中产品发现、定义、设计、策略、平台建设或产品经营时，读取 references/role-guides/product.md。
  • 岗位属于 AI 产品、策略产品、商业化产品、C 端产品、商业分析、数据分析、市场、GTM、产品运营 或策略运营时，读取 references/role-guides/multi-role-decision-scenes.md 的对应小节；复合岗位 只读取真正相关的最小组合。
  • 岗位画像明确命中 AI 产品、AIGC、大模型、Agent、模型/数据平台、AI+行业产品，或 JD 以 AI 产品判断、设计和落地为核心时，在产品指南之后读取 references/role-guides/ai-product.md。岗位实际偏算法、工程、销售、交付或运营时，不因 标题含“AI”强制触发。
  • 上述岗位或用户核心经历进一步强调 AI Coding、技术原型、应用搭建、Spec/SDD、API/数据库、 测试、部署或生产交付时，再读取 references/role-guides/ai-coding-product-delivery.md；只要求术语理解不足以触发。
  • JD 明确涉及广告、投放、变现、商业产品、营销平台、会员/内购或商业中后台时，读取 references/role-guides/commercialization-product.md；只出现“商业价值”不足以触发。
  • 检索小红书和牛客前读取 chrome:control-chrome Skill，使用用户已有登录态。
  • 读取 PDF、DOCX 等上传材料前读取对应文档 Skill。
  • 用户选择从飞书读取经历深挖产物时，读取 lark-wiki 和 lark-doc Skill；只列候选标题， 用户明确选择后才读取正文。
  • 需要保存飞书时，读取 lark-wiki、lark-doc 和同级隐藏目录 ../.offerloop-runtime/references/artifact-contract.md；脚本位于 ../.offerloop-runtime/scripts/artifact_contract.py。
  • 岗位或 JD 明确涉及 AI、AIGC、大模型、Agent、算法能力，或用户直接要求准备 AI 术语突袭 时，从当前 Skill 定位兄弟 experience-deepthink，读取其 references/ai-concept-glossary.md。直接概念题优先按“一句话本质—简明机制—相邻概念 对比—PM 决策点”组织成 30–60 秒回答；有个人材料时再连接一条真实经历证据，不为连接而 编造项目。

Read the full file on GitHub · 266 lines

Files

What ships with it

16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 5d ago First seen · 266 lines · 255 tokens per session scan A 13e12136f527

Subscribe to this mod's changes

interview-prep is a skill published in the GitHub repository riwonswain-ovo/OfferLoop (16 stars, last pushed 3d ago), licensed MIT. It adds 255 tokens to every session and 4,686 once invoked, about $0.0013 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.

Learning and teachingagent-skillsai-careercareer-developmentclaude-codecodexfeishu
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