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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

760

周安装

32

GitHub Stars

公开资料未说明

下载量

266
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

3

许可证

MIT

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/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

简介

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。

SKILL.md

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:

  • CodebaseGlob / Grep / Read, or Agent with the Explore subagent for surveys spanning many files
  • Existing docsRead files the user already referenced
  • WebWebSearch 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

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

04

需要参考平台分布和安装热度时

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

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

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 warning/failed 结果,不能写成本站确认安全。

来源信息

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