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interview-analysis访谈分析

Agent Skill

interview-analysis 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

915

周安装

37

GitHub Stars

公开资料未说明

下载量

287
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-analysis(访谈分析)
来源仓库:https://github.com/mikonos/interview-analysis
仓库路径:skills/interview-analysis
安装命令:
npx skills add https://github.com/mikonos/interview-analysis --skill interview-analysis
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/mikonos/interview-analysis --skill interview-analysis

简介

interview-analysis 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。
  • 通过 npx skills add 命令从指定仓库安装,需确认权限和维护状态。
  • 使用前建议核实是否会触发联网、命令执行或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Interview Analysis Skill

Core Mission: Transform interview transcripts into deep insights. Core Logic: Don't listen to what candidates "say" (Methodology Recitation), observe what they've "done" (Battle Scars) and "how they think" (First Principles).

1. Dynamic Expert Activation (Expert Routing)

Core Principle

Based on role type and evaluation dimensions, automatically select the best minds combination for that domain:

Three-Step Expert Selection:

  1. Identify core competency domain: Product/Engineering/Operations/Design/Sales/Data Science/...
  2. Match top domain thinkers: Recognized methodology masters or practitioners in the field
  3. Combine hiring experts: Geoff Smart (fact-checking) + Lou Adler (competency validation)

Common Role-Expert Mapping (Non-Exhaustive)

Role TypeDomain Expert (Methodology)Hiring Expert (Validation)Rationale
Product ManagerMarty Cagan / Julie ZhuoGeoff SmartProduct Sense + Fact Check
Software EngineerLinus Torvalds / John CarmackLou AdlerEngineering Judgment + Results Validation
Growth HackerSean Ellis / Brian BalfourGeoff SmartGrowth Methodology + Metrics Verification
UX DesignerDon Norman / Jony IveLou AdlerUX Principles + Portfolio Validation
Data ScientistAndrew Ng / DJ PatilGeoff SmartTechnical Depth + Project Verification
OperationsSheryl Sandberg / Reid HoffmanLou AdlerScale Operations + Results Focus
Sales/BDAaron Ross / Jill KonrathGeoff SmartSales Methodology + Performance Verification
[!IMPORTANT] Flexibility Principle: The table above is for reference only. Flexibly select the most appropriate expert combination based on specific role and candidate background. Encourage Innovation: If you believe a non-mainstream expert is better suited to evaluate this candidate, make that choice and explain your rationale. Core Question: "Who can best identify imposters in this role? Whose framework best validates core competencies?"

2. Execution Workflow

Step 1: Fact Reconstruction & Red Flag Scan

  • Timeline Reconstruction: Connect experiences scattered across multiple interview rounds, checking for logical gaps.
  • Consistency Verification: Compare different versions of the same story told to different interviewers (e.g., reasons for leaving, project failures).
  • Red Flag Annotation: Mark all vague titles (e.g., SPM), exaggerated data, and attribution fallacies ("it was all market/technology's fault").

Step 2: Deep Decoding - STAR Episodes

  • Tactic: Select 1-2 core cases (e.g., startup project, most challenging project) for microscopic analysis.
  • Truth Extraction:

- Methodology Check: Is the candidate reciting SOPs (MECE, SWOT) or applying first principles? - Solution Bias Check: Did they jump straight to "add features," or first conduct "value validation"? - Technical Boundary Check: For technical challenges, did they "deflect blame" or "anticipate"?

Step 3: Interviewer Meta-Analysis

  • Subject: Evaluate interviewer (you/colleagues) performance.
  • Dimensions:

- Depth: Did they probe at critical moments? Or let it pass? - Bias: Did they draw conclusions too early or ask leading questions? - Bar: Did they maintain A Player standards?

Step 4: Card-based Output (Zettelkasten Output)

Generate Markdown cards using the following standard templates, saved to people/{candidate_name}/analysis/. Be sure to read template content before filling in analysis results.

  • Profile (Comprehensive Portrait):

- Template path: templates/profile_template.md - Purpose: Fact checking, red flag scanning, core competency assessment.

  • Insight (Deep Analysis):

- Template path: templates/insight_template.md - Purpose: Deep dive into specific domains (e.g., AI Capability, Product Strategy).

  • Meta-Analysis (Interviewer Review):

- Template path: templates/evaluation_template.md - Purpose: Evaluate interviewer performance and organizational recommendations.

  • Structure Note (Hub Document):

- Template path: templates/structure_note_template.md - Purpose: Serves as hub connecting all analysis cards above, forming decision closure.

3. Usage Examples

  • "Analyze Li Yashuang's three interview rounds, focusing on AI capabilities."
  • "Review this interview to see where we interviewers did well and where we missed opportunities."
  • "Use Marty Cagan's perspective to analyze this candidate's product thinking."

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

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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

39.41%
按下载量换算113

Claude

29.2%
按下载量换算84

Cursor

19.5%
按下载量换算56

Gemini CLI

8.55%
按下载量换算25

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

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