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interview-designer采访设计师

Agent Skill

interview-designer 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。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

68,471

周安装

2,797

GitHub Stars

4

下载量

21,928
OpenClaw

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

MIT-0

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-designer(采访设计师)
来源仓库:https://github.com/mikonos/interview-designer
安装命令:
openclaw skills install interview-designer
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install interview-designer

简介

使用基于证据的方法分析简历并设计面试策略。将面试准备从“阅读简历→提问”转变为“定义标准→取证证据→未来模拟”。结合了杰夫·斯马特(Geoff Smart)的顶级分级、卢·阿德勒(Lou Adler)的基于绩效的招聘以及丹尼尔·卡尼曼(Daniel Kahneman)的偏见控制。在准备面试、创建结构化面试指南或设计问题以验证候选人能力时使用。

SKILL.md

name
interview-designer
description
Analyze resumes and design interview strategies using evidence-based methodology. Transforms interview prep from "read resume → ask questions" into "define standard → forensic evidence → future simulation". Combines Geoff Smart's Topgrading, Lou Adler's performance-based hiring, and Daniel Kahneman's bias control. Use when preparing for interviews, creating structured interview guides, or designing questions to validate candidate competencies.

Interview Designer Skill

Core Mission: Elevate interview planning from "glancing at resume and asking questions" to "evidence-based investigation and projection." Operating Mechanism: Define Scorecard (set standards) → Forensic Scan (evidence gathering) → Future Simulation (performance prediction). Prompt Strategy: This skill uses \<Chain of Thought\>. When executing, maintain an "Objective Evaluator" perspective, seeking both Red Flags and Green Signals.

1. Dynamic War Room (Expert Panel)

Dynamically summon the most matching best minds into the war room based on candidate's role attributes:

  • Geoff Smart (Who): Responsible for Define & Verify.

* *Principle*: Scorecard First. Before looking at any resume, clarify what the standard for an "A Player" is.

  • Lou Adler (Performance-based): Responsible for Predict.

* *Principle*: Past performance predicts future performance *only if* the context is similar. Must design simulations for future scenarios.

  • Daniel Kahneman (Bias Control): Responsible for De-bias.

* *Principle*: Beware of "confirmation bias." If concerns are found, also seek counter-evidence; if highlights are found, verify their replicability.

  • Domain Expert: Responsible for Depth.

2. Core Execution Workflow

Step 1: Scorecard Definition - *Smart's Priority*

Don't look at the resume first! Based on JD or role requirements, define A Player standards for this position:

  • Mission: One sentence - why does this role exist?
  • Outcomes: 3-5 specific, measurable results that must be achieved within 12 months.
  • Competencies: Hard/soft skills required to achieve the above outcomes.

Step 2: Forensic Resume Scan - *Smart's Forensic*

Use Step 1 standards to scan the resume, looking for Gaps (discrepancies) and High Points (highlights):

  • The "Too Good To Be True" Heuristic: Logical gaps behind perfect data.
  • The "Passenger vs Driver" Heuristic: Individual's true contributions under big company halo.
  • The "First Principles" Heuristic: Principle understanding behind technical jargon.

Step 3: Pressure Test & Future Simulation - *Adler's Prediction*

Design two types of questions:

  1. Pressure Test Scripts (for past): Design Forensic STAR follow-ups targeting Step 2 concerns (originally "torpedo questions," but more objective).
  2. Future Simulation (for future): Design a specific Performance Problem.

* *Example*: "We're entering this new market next year, and the biggest obstacle is X. If you join, how would you analyze this problem in your first week?"

3. Question Design Principles

  1. Cannot Be Memorized: Forces candidates to think on the spot (Simulation) or recall painful memories (Pressure Test).
  2. Forced Trade-offs: Choose between two "correct" options to test values.
  3. Detail Granularity: Must be able to probe down to "what diagram did you draw" or "what exact words did you say."

4. Output Format

Directly call templates/interview_guide_template.md to generate the report. Note: When generating the guide, include both [Red Flags] (concerns) and [Green Signals] (highlight verification) to maintain objectivity in assessment.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

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能力 2

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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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

77.19%
按下载量换算16,926

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

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

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来源信息

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