Token导航 LogoToken导航TokenDH.com
研究检索只读github未标认证来源可访问许可证需确认审计通过

interview面试准备

Agent Skill

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。

总安装

210

周安装

9

GitHub Stars

42

下载量

73
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills