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Agent Skill · 研究检索

interview(面试准备)

interview 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

interview 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 70,604。

总安装 210周安装 9GitHub Stars 42下载 73核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:interview(面试准备)
来源仓库:https://github.com/panaversity/claude-code-skills-lab
仓库路径:skills/interview
安装命令:
npx skills add https://github.com/panaversity/claude-code-skills-lab --skill interview
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/panaversity/claude-code-skills-lab --skill interview

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

interview 用于查找、检索和筛选相关信息,支持多宿主环境快速定位候选结果。适合在关键词搜索、任务场景匹配或来源线索梳理时使用。可结合原始 README 和安装命令进一步验证具体用法。使用前应确认权限范围、维护状态及是否涉及联网或文件操作。建议核对来源仓库状态,避免推荐未经安全评估的第三方能力包。

来源仓库内路径:skills/interview

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/panaversity/claude-code-skills-lab --skill int…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

interview 怎么安装?

在终端执行 npx skills add https://github.com/panaversity/claude-code-skills-lab --skill interview。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

interview 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

interview 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

interview 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

interview 用的人多吗?

公开数据是累计安装 210 次,近一周安装 9 次,下载 73 次,GitHub 42 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

interview 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
interview

面试准备 · 本页

21042只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Interview Skill

Prevent building the wrong thing. Discover user's intent (WHY), validate assumptions, and agree on approach (WHAT) before taking action.

What This Skill Does

  • Discovers INTENT behind surface requests (WHY they want it)
  • Surfaces and validates AI's assumptions before acting
  • Explores solution options informed by intent
  • Reaches mutual agreement on both problem and solution
  • Works for any context: software, documents, brainstorming, automation

What This Skill Does NOT Do

  • Follow rigid scripts
  • Skip to implementation without understanding
  • Accept surface requests without exploring intent
  • Make assumptions without validating them

Core Problem This Skill Solves

AI builds the wrong thing because it:

  1. Takes surface requests literally without understanding intent
  2. Makes hidden assumptions it never validates
  3. Proceeds without confirming alignment

This skill ensures:

  1. Intent (WHY) is discovered, not just request (WHAT)
  2. Assumptions are surfaced and validated
  3. Both problem and solution are agreed before proceeding

The WHY + WHAT Model

Surface WHAT → Discover WHY → Surface Assumptions →
Informed WHAT → Agree on Both → Proceed
PhasePurposeExample
Surface WHATCapture initial request"Add dark mode"
Discover WHYUncover intent/problem"Eye strain for night workers"
Surface AssumptionsExpose AI's hidden assumptions"Assuming web app, not mobile"
Informed WHATSolution options based on WHY"Dark mode + auto-brightness + schedule"
Agree on BothConfirm problem AND solution"Solving eye strain via dark mode with auto-switch"

When to Trigger

TriggerExample
Explicit invocation/interview, "let's clarify"
Request could be misunderstoodAmbiguous, complex, or multi-part requests
Recommendations needed"What should I use for..."
Brainstorming"Help me think through..."
High-stakes workWhere wrong output wastes significant effort

Don't over-trigger: Simple, clear requests don't need full discovery.


Discovery Flow

Before Starting

Gather available context before asking questions:

SourceGather
ConversationUser's stated request, prior context
Available ContextInformation already shared in session
Skill ReferencesQuestion patterns from references/

1. Surface WHAT

Capture the initial request clearly.

"Let me make sure I understand - you're asking for [X]?"

2. Discover WHY

This is the critical step most AI skips.

Go beyond WHAT to understand WHY:

AskTo Discover
"What problem does this solve?"The real need
"Why now?"Urgency and context
"What happens if we don't do this?"Stakes and priority
"Who benefits and how?"Users and value
"What led to this request?"Background and triggers

Techniques for WHY:

Laddering - Dig into abstract goals:

"Dark mode" → "Why?" → "Eye strain" → "Why an issue?" → "Night shift workers"

5 Whys - Uncover root need:

"Export feature" → Why? → "Share reports" → Why? → "Stakeholder reviews" → Root need

Structuring Clarifications:

When presenting multiple questions, distinguish must-know from nice-to-know:

## Required Clarifications
1. [Critical question - blocks progress]
2. [Critical question - affects core approach]

## Optional Clarifications (if relevant)
3. [Nice-to-know - can assume reasonable default]

Note: Keep to 1-4 questions per round. Build on answers.

3. Surface Assumptions

This prevents "builds wrong thing."

AI always makes assumptions. Surface them explicitly:

"I'm assuming:
- This is for [platform/context]
- Users are [type]
- We need to support [X] but not [Y]
- [Other assumption]

Are these correct?"

Common hidden assumptions:

  • Technology/platform
  • User expertise level
  • Scale/performance needs
  • Integration requirements
  • What's in vs out of scope

4. Informed WHAT

Now that WHY is clear, explore WHAT options:

"Given that you need [WHY], we could:
1. [Option A] - [trade-off]
2. [Option B] - [trade-off]
3. [Option C] - [trade-off]

Which fits your intent best?"

Key: Options should address the WHY, not just the surface WHAT.

5. Agree on Both

Confirm understanding of BOTH problem and solution:

## Understanding

**Problem (WHY)**: [What we're solving and why it matters]

**Solution (WHAT)**: [What we'll build/do]

**Key decisions**:
- [Decision 1]
- [Decision 2]

**Not included**: [Explicit scope boundaries]

Does this capture it correctly?

Only proceed after explicit confirmation.


Depth Check

How do you know understanding is deep enough?

Surface Understanding (NOT enough)

  • Can repeat what user asked for
  • Know the immediate request
  • Haven't explored why

Deep Understanding (ENOUGH)

  • Know WHY they want it, not just WHAT
  • Know what problem it solves
  • Assumptions are surfaced and validated
  • Know who benefits and how
  • Know what's explicitly out of scope
  • Could explain it to someone else accurately
  • User confirmed understanding is correct

Test: If you proceeded now and built something, would user say "yes, that's what I meant" or "no, you misunderstood"?


Assumption Categories

Surface assumptions in these areas:

CategoryExample Assumptions
ContextPlatform, environment, existing systems
UsersWho they are, expertise level, needs
ScaleVolume, performance requirements
ScopeWhat's included vs excluded
QualityStandards, constraints, requirements
TimelineUrgency, phases, dependencies

Anti-Patterns

Anti-PatternWhat HappensFix
Skip WHYBuild wrong solutionAlways ask why before how
Hidden assumptionsSurprise misalignmentSurface and validate explicitly
Accept surface requestMiss real needDig deeper with laddering/5 whys
Proceed without confirmWaste effortGet explicit "yes, proceed"
Over-question simple requestsAnnoy userMatch depth to complexity

Tool Adaptation

Use whatever tools are available:

GoalApproach
Ask questionsInteractive tools if available, otherwise conversation
Research contextWeb search if needed and available
Present optionsStructured choices if available

The skill describes WHAT to do. The agent uses available tools.


Output: Understanding Summary

Match formality to situation:

Quick (simple requests):

Got it: [WHAT] to solve [WHY]
Proceeding with [approach]. Confirm?

Standard (most cases):

## Understanding

**Problem (WHY)**: [Intent and problem being solved]
**Solution (WHAT)**: [What we'll do]
**Key points**: [Important details]
**Not included**: [Scope boundaries]

Ready to proceed?

Detailed (complex work): See references/summary-templates.md


Quick Reference

1. Surface WHAT → "You're asking for X?"
2. Discover WHY → "What problem does this solve?"
3. Surface assumptions → "I'm assuming A, B, C - correct?"
4. Informed WHAT → "Given WHY, we could do X, Y, or Z"
5. Confirm both → "So we're solving [WHY] by doing [WHAT]?"
6. Proceed → Only after explicit confirmation

Reference Files

FilePurpose
references/question-patterns.mdTechniques for discovering WHY and surfacing assumptions
references/anti-patterns.mdCommon mistakes that lead to building wrong thing
references/summary-templates.mdOutput formats for different situations

平台分布

Codex

35.11%
按下载量换算26

Claude

30.07%
按下载量换算22

Cursor

19.19%
按下载量换算14

Gemini CLI

10.1%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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