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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 73,674 个 Skill 里排第 69,749。

总安装 216周安装 9GitHub Stars 2下载 72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/lev-os/agents
仓库路径:skills/interview
安装命令:
npx skills add https://github.com/lev-os/agents --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/lev-os/agents --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,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。适用于面试题库查询、岗位技能要求分析和候选人评估资料筛选等招聘支持场景。

通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和联网需求。建议结合原始 README 核验具体用法,注意维护状态及是否触发文件读写操作。interview 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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/lev-os/agents --skill interview;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/lev-os/agents --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 216 次,近一周安装 9 次,下载 72 次,GitHub 2 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

2162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: Wizard-Mode Questioning

Framework-powered interactive decision-making. Guides users through complex problems using strategic, creative, and philosophical frameworks.

Quick Start

/interview                    # Start new interview session
/interview --framework=SCAMPER  # Start with specific framework

Hard Requirements (Non-Optional)

  1. Research between every question — run fresh evidence-gathering before each new question.
  2. Work lifecycle coupling — when .lev/pm/ exists, treat interview as part of work lifecycle and update active artifacts.
  3. Context-rich questions only — never ask "ingredient-only" option prompts without decision context and consequences.
  4. Trade-off transparency — always surface trade-offs, validation-gate impact, and side effects.

How It Works

  1. Context Analysis - Detect complexity and classify decision level (strategic/integrative/tactical).
  2. Framework Selection - Choose one primary framework and one cross-check lens.
  3. Per-Question Research (required) - Before each question, collect new evidence from workspace, prior answers, and constraints.
  4. Lifecycle Sync (required with work) - Track entities/decisions and iterate handoff/spec/proposal docs as context evolves.
  5. Question Loop (reciprocal) - Ask one structured question, answer one reciprocal user question, then continue.
  6. Decision Assimilation - After each answer, update artifacts and convert resolved questions into explicit decisions.
  7. MBTI Overlay (optional) - Use only when perspective-testing is useful.

Per-Question Research Protocol (Required)

Before each question, do all of:

  1. Pull evidence from current workspace context (specs, handoffs, plans, code).
  2. Check active constraints and quality gates.
  3. Validate whether the next question still matters based on newly discovered facts.

Minimum evidence set per question:

  • One artifact fact from .lev/pm/ (spec/handoff/proposal/plan).
  • One implementation or architecture fact from code/docs.
  • One validation constraint from .lev/validation-gates.yaml when present.

If no new evidence exists, explicitly state that and why the next question is still needed.

Work Lifecycle Coupling (Required when .lev/pm/ exists)

Interview mode is not standalone Q&A. It is a foreground intake loop inside work DISCOVER/ALIGN.

When active:

  1. Locate active handoff under .lev/pm/handoffs/ and keep it current.
  2. Track entities (state changes, blockers, decisions) using lifecycle language.
  3. Iterate affected docs after each major answer:

- specs (.lev/pm/specs/) - proposals (.lev/pm/proposals/) - handoffs (.lev/pm/handoffs/)

  1. Convert resolved open questions into decisions with rationale and consequence.
  2. Carry unresolved items forward with explicit owner/next action.

Degrade gracefully if artifacts are missing, but do not skip lifecycle sync silently.

Session Handoff Bootstrap (Hard Rule)

If there is no active session handoff, the interview must not proceed as normal Q&A.

Required behavior:

  1. Load $work and run DISCOVER lifecycle preflight.
  2. Create or update the active handoff under .lev/pm/handoffs/.
  3. Record stage, objective, and initial evidence before asking decision questions.
  4. Resume interview loop only after handoff continuity exists.

Enforcement:

  • If $work cannot be loaded, stop and ask the user to resolve the blocker.
  • Do not run fallback interview behavior without work lifecycle initialization.

Framework Library

Strategic/Systems Thinking

  • SWOT, First Principles, Systems Thinking, Theory of Constraints
  • McKinsey 7S, Cynefin, Jobs to Be Done

Creative Thinking

  • SCAMPER, Reverse Brainstorming, Extreme Users
  • Six Thinking Hats, Figure Storming, TRIZ, Design Thinking

Philosophical/Spiritual

  • Taoist Wu Wei, Ubuntu, Ayurvedic Doshas
  • Buddhist Non-Attachment, Sufi/Rumi Poetry, I Ching, Stoicism

Psychological/Cognitive

  • MBTI (8 Cognitive Functions), Big Five (OCEAN), Ladder of Inference

Output Format

Single question with grounded options:

q1) {Question title}
🎯 Decision Context:
- {What decision is being made}
- {Why this decision is needed now}
- {What gets blocked/unblocked}
- {What could break if wrong}

🧠 {Framework Name} Analysis:
{Framework-based interpretation of the decision and pressure points}

🔎 Evidence (research run this turn):
- {Prior answer + artifact evidence with path}
- {Code/doc fact relevant to this decision}
- {Validation gate(s) impacted from .lev/validation-gates.yaml}
- {Constraint/conflict/assumption check}

⚖️ Trade-off Summary:
| Option | Benefits | Costs/Risks | Gate Impact | Side Effects |
|---|---|---|---|---|
| a | ... | ... | ... | ... |
| b | ... | ... | ... | ... |
| c | ... | ... | ... | ... |  # optional

Options (2-3 researched):
a. {Concrete option A with trade-off}
b. {Concrete option B with trade-off}
c. {Concrete option C with trade-off}  # optional

✅ Recommended: {Option letter} — {Why this best fits evidence and constraints}

🤝 Reciprocal:
User may ask one question; answer directly before next interview question.

🧭 Follow-Up Menu:
1. [{Action for option A}]
2. [{Action for option B}]
3. [{Alternative perspective}]
4. [Deep dive into trade-offs]
5. [Go back]
6. [Switch framework]

Integration

  • Command: /interview (formerly /guideme)
  • Skill Route: skill://interview
  • Used by: work skill for wizard-mode context gathering
  • Lifecycle Hook: In repos with .lev/pm, interview must update entities/handoffs/docs as part of work progression
  • Gate Awareness: Interview must consult .lev/validation-gates.yaml for option-level compliance and side effects

Status: v1.2.0 Migrated from: /guideme command

Technique Map

  • Identify scope — Determine what the skill applies to before executing.
  • Follow workflow — Use documented steps; avoid ad-hoc shortcuts.
  • Ground every question — No question without fresh per-turn evidence from context/research.
  • Show research trace — Make evidence visible in every question.
  • Update lifecycle artifacts — Maintain handoff/spec/proposal continuity when .lev/pm is active.
  • Recommend explicitly — Always include a best option and rationale.
  • Expose trade-offs — Include gate impact, side effects, and blast radius.
  • Use reciprocity — Handle one user counter-question before advancing.
  • Verify outputs — Check results match expected contract.
  • Handle errors — Graceful degradation when dependencies missing.
  • Reference docs — Load references/ when detail needed.
  • Preserve state — Don't overwrite user config or artifacts.

Technique Notes

Skill-specific technique rationale. Apply patterns from the skill body. Progressive disclosure: metadata first, body on trigger, references on demand.

Prompt Architect Overlay

Role Definition: Specialist for interview domain. Executes workflows, produces artifacts, routes to related skills when needed.

Input Contract: Context, optional config, artifacts from prior steps. Depends on skill.

Output Contract: Artifacts, status, next-step recommendations. Format per skill.

Edge Cases & Fallbacks: Missing context—ask or infer from workspace. Missing lifecycle artifacts—create minimal continuity notes and continue. Dependency missing—degrade gracefully and state what could not be validated. Ambiguous request—clarify before proceeding.

平台分布

Codex

36.52%
按下载量换算26

Claude

31.25%
按下载量换算23

Cursor

18.83%
按下载量换算14

Gemini CLI

9.78%
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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