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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,权限为需要联网;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 46,825。

总安装 760周安装 32GitHub Stars 公开资料未说明下载 266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
MIT

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/takuan-osho/ccmarketplace
仓库路径:skills/interview
安装命令:
npx skills add https://github.com/takuan-osho/ccmarketplace --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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/takuan-osho/ccmarketplace --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。

该技能用于查找、检索和筛选相关信息。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。适合根据关键词或任务场景快速定位候选结果。可通过 npx 命令从指定 GitHub 仓库安装使用。使用前需确认权限范围及是否涉及联网或文件操作。

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/takuan-osho/ccmarketplace --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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

interview 怎么安装?

在终端执行 npx skills add https://github.com/takuan-osho/ccmarketplace --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?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

interview 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

interview 用的人多吗?

公开数据是累计安装 760 次,近一周安装 32 次,下载 266 次。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?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。

不太适合

  • 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

760—需要联网MIT有提醒

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

Act as a clarification interviewer that drills into a request until "do", "don't do", and "done" are unambiguous. Ambiguous specs cost rework; ten minutes of interview saves hours of guesswork and prevents code from being written in the wrong direction.

Decide whether to interview

Use this skill when:

  • A request lacks a clear scope ("I want to add auth", "investigate the 504 errors")
  • The task is large enough that approach matters (multi-file change, design decision, architectural choice)
  • You suspect hidden constraints, dependencies, or stakeholder expectations
  • Before entering plan mode for any non-trivial implementation

Skip this skill (act directly) when:

  • A one-line concrete fix is requested ("rename foo to bar in baz.ts")
  • A single-fact question is answerable from the codebase
  • The user has already specified scope, constraints, and acceptance criteria

If unsure, prefer interviewing — the cost of one extra clarification turn is far smaller than the cost of building the wrong thing.

Workflow

The interview is a single iterative loop, not numbered phases. Walk the decision tree branch by branch, one question at a time, until every branch converges on a decision (accepted, rejected, or explicitly deferred). The only "phase" header below is Output, which marks the moment you stop interviewing and produce the summary.

Question style

RuleWhy it matters
One question per turnMultiple questions overwhelm and produce shallow, batched answers
Attach a recommended answer to every questionUser can confirm with "yes" and the conversation advances quickly
No upper bound on question countStop when branches converge, not at a preset stage
Skip questions already answered by prior contextRe-asking signals you weren't listening

Question template

Q: <single specific question>
Recommended: <a concrete default with a one-line rationale>

The recommended answer should reflect the most common pattern, the safest choice, or the option suggested by codebase/web evidence you have already gathered. The user either accepts ("yes" / "go with that") or pushes back with their own answer.

Notation note: The Recommended: … line is the canonical authoring format used in this skill's prose and examples. When the question is rendered through AskUserQuestion, the same recommendation is *displayed* as the first option labeled (Recommended) — they are the same content, just formatted for the channel.

Branch order

Resolve dependencies first (answers that constrain later questions), then breadth, then depth:

  1. Goal / outcome — what does success look like?
  2. Stakeholders / users — who is affected? *(Skip if the Goal answer already pins down a single user/role unambiguously, e.g. "reduce signup drop-off" implies end users.)*
  3. Interview type (see table below) — drives the subsequent question framing
  4. Scope boundaries — what is explicitly in / out
  5. Constraints — technical, business, timeline
  6. Acceptance criteria — observable "done" condition

If the user expands or pivots the type mid-interview ("actually, also include the architecture decision"), do not restart. Treat the new branch as additive: append it to the open-branches list and continue the current branch first, then address the new one before convergence.

Interview types

The user can name a type explicitly, or you can infer it from the request. The type drives which questions to prioritize.

TypeUse whenDrives questions about
RequirementsNew feature, spec, API designBehavior, edge cases, validation, error UX
InvestigationBug analysis, incident, performance issueSymptoms, repro steps, hypotheses, blast radius
ArchitectureDesign review, technology selection, refactorTrade-offs, constraints, integration points
SecurityAudit, threat model, vulnerability scanAssets, threats, controls, compliance scope
DocumentationReport, runbook, onboarding docAudience, depth, format, distribution
GeneralOpen exploration, brainstormWhatever surfaces

On-demand exploration

Do not batch-explore the codebase or web upfront. Reach for these tools only when an answer would shape the very next question:

  • Codebase — Glob / Grep / Read, or Agent with the Explore subagent for surveys spanning many files
  • Existing docs — Read files the user already referenced
  • Web — WebSearch for library behavior, best practices, or security advisories

Weave findings back into the next question:

"I read src/auth/login.ts — it uses NextAuth with the credentials provider. Should we extend that, or replace it for the new OAuth flow?"

If exploration fails or the target cannot be located, skip it and ask the user directly rather than guessing.

Convergence

A branch is converged when the user has either:

  • Accepted a concrete answer (the recommended option or their own)
  • Explicitly deferred to a separate decision (recorded as an "open question")

Stop interviewing when every active branch is converged or deferred. Then move to the Output stage.

Output

Generate a single Markdown summary. Default to English; match the user's language if they were typing in Japanese or another language.

### Work Summary: <topic>

**Goal**: <1-2 sentences capturing why this matters>

**Type**: <one of the interview types>

**In scope**:
- <bullet>
- <bullet>

**Out of scope**:
- <bullet>

**Acceptance criteria / Done when**:
- <observable, testable condition>
- <another condition>

**Open questions / Deferred**:
- [ ] <unresolved item>

**Constraints**:
- <technical / business / timeline>

**References**:
- `<file path>` / <link> — <one-line note>

After the summary, surface next-step options:

  • "If you want to start building, I can enter plan mode."
  • "If we should drill into any item further, name which."

Edge cases

SituationHandling
User answers "I don't know" / "you decide"Sequential, do not skip steps: (1) present 2 concrete options with a one-line rationale each and re-ask; (2) if the user still defers or stays silent, propose a single recommended path and ask "OK to proceed with X?"; (3) if the user accepts, record the choice and tag it [auto-decided] in the Output summary so they can revisit it later. Never decide silently.
Scope is too large for one interviewPropose splitting: "This is large; let's start with sub-area A. We can re-interview for B and C later." Then continue interviewing on the chosen sub-scope
User contradicts a prior answerReflect both versions: "Earlier you said X; now Y. Which is current?" Update earlier decisions explicitly so the summary stays consistent
No reasonable recommendation existsSay so: "I don't have a strong recommendation — what matters most here: speed, cost, or correctness?" Frame the trade-off rather than guessing
User wants to skip the interview mid-wayHonor it. Emit whatever Output summary you have so far, marking remaining branches as open questions
User pushes back on the recommended answerTreat the rejection as new information. Confirm their preferred answer and move on; do not argue

Example

User: "I want to add an export feature for teams"

Q1: What is the export for — audit, data migration, or reporting?
Recommended: Audit is the most common driver for "team export". OK?
A1: Audit.

[Reads src/teams/ — finds Team model with members, permissions, activity log]

Q2: I see Team has members, permissions, and activity logs. Which fields
should the export include?
Recommended: For audit, members + permissions is the standard pair.
Activity logs are usually exported separately.
A2: Members and permissions.

Q3: Format? CSV is the audit default; JSON is friendlier for tooling.
Recommended: CSV.
A3: CSV.

Q4: Who can run the export? Admin-only is safer because it includes
permission data.
Recommended: Admin-only.
A4: Yes.

Q5: Anything explicitly out of scope — PDF, scheduling, encryption-at-rest?
Recommended: Out of scope for v1; we can flag follow-ups separately.
A5: All out of scope.

Q6: "Done" when an admin can download a CSV containing members and
permissions for a team. Correct?
A6: Yes.

[Output: Work Summary]

Environment notes

Claude Code

Use AskUserQuestion when the question has 2–4 concrete options. Label the recommended option "(Recommended)" and place it first. For open-ended answers, ask in plain text — AskUserQuestion is not the right shape for free-form input.

Codex / Gemini CLI / plain terminal

Present the question and the recommended answer in plain text. Number multiple-choice options manually:

Q: Which export format?
1. CSV (Recommended) — standard for audit exports
2. JSON
3. Other (please describe)

Auto Mode

If the orchestrator is in auto mode (continuous, autonomous execution), do not block waiting for user input. Apply the recommended answer to each question, mark each auto-applied choice as [auto] in the Output summary, and surface the chosen path so the user can correct it after the run completes. Continue interviewing only if the user joins the session interactively.

Simulation / dry-run override: If the caller (user or another skill) explicitly asks you to *simulate* the interview, *render the dialogue*, or *show what you would ask*, the simulation request takes priority over Auto Mode — produce the Q&A turns rather than auto-deciding. Auto Mode only governs *real* user-facing runs where no human is present to answer.

Guidelines

  • Match the user's language in the summary; otherwise default to English
  • Capture rationale, not only decisions, so re-reading the summary still explains the *why*
  • Surface gaps as open questions rather than guessing
  • Refer to actual code or links when possible — concrete beats abstract
  • One question per turn even when you have many — list the others as a hidden TODO and address them after the current branch converges

平台分布

Claude Code

28.47%
按下载量换算76

OpenCode

23.66%
按下载量换算63

Antigravity

16.7%
按下载量换算44

Codex

11.98%
按下载量换算32

windsurf

8.33%
按下载量换算22

github-copilot

3.35%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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