mock-interview

REVIEW · 67
Registry indexed

Runs a realistic mock interview in chat — builds an interviewer persona from the intel file, asks one question at a time with pressure follow-up chains, then scores the candidate on structured delivery, evidence density, role fit, follow-up resilience and recitation-smell, writin

Verified installs0
Stars11
Version1.0.0
Quality58/100 · Promising
Trust67/100 · Sandbox only
Audit78/100 · Needs review

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

Research agents

I need my agent to research a topic, compare sources, and produce a concise report.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add YunyueLi/greenroom --skill mock-interview

Maintenance

fresh

Pushed today

Risk

Needs review

Low GitHub adoption signal

GitHub quality

11

58/100 Quality · 75/100 Trust

Coverage tags

ResearchResearch agentsagent-skill

Review notes

Low GitHub adoption signal · Quality score needs review

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
58

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
67

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
78

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

11 GitHub stars

Repo activity

11 stars, 1 forks

Maintenance

Pushed today

License

AGPL-3.0

Install

npx skills add YunyueLi/greenroom --skill mock-interview

Install safety

standard package or runtime install path

Permission surface

filesystem or document access

Agent outcomes

No agent outcome data yet

Docs

Usable metadata, review docs

Risk summary

Review before production

  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 11 GitHub stars
  • Stars/forks activity: 11 stars, 1 forks; issue activity unavailable in current metadata

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Search sources

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add YunyueLi/greenroom --skill mock-interview
Policy
review
Human review
yes

Trust and risk

Trust
67/100
Audit
78/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add YunyueLi/greenroom --skill mock-interview

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Low GitHub adoption signal
  • No OpenAgentSkill engagement data yet
  • Quality score needs review

Agent safety v2

62/100 · Review before install

Reviewed with permission notesreview

Usable candidate, but the agent should surface permission and audit notes before installation.

Require human approval before installing into a real workspace.

Resolve via API

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

  • Low GitHub adoption signal

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install yunyueli-mock-interview

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use mock-interview in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20mock-interview%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/yunyueli-mock-interview/install
Install command: npx skills add YunyueLi/greenroom --skill mock-interview
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use mock-interview for this task. Review https://www.openagentskill.com/api/skills/yunyueli-mock-interview/install, then install with: npx skills add YunyueLi/greenroom --skill mock-interview

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

57/100

Research agents

Platforms

Claude Code

Audit report

Needs review · 78/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Needs validation for Research agents

Do a manual repository review before adding this to an agent workflow.

57
Readiness
Review
Stage

Role in stack

Needs validation

Primary fit

Research agents

Trust label

Needs manual review

Install path

Command ready

Use when

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 58/100 quality profile

review first

  • Low GitHub adoption signal
  • No OpenAgentSkill engagement data yet

Implementation path

  1. 1Install it in a sandbox agent and run one Research agents task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

67
OpenAgentSkill Trust Score

GitHub adoption

FIX

11 GitHub stars

Stars/forks activity

FIX

11 stars, 1 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

AGPL-3.0

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 11 GitHub stars
  • Stars/forks activity: 11 stars, 1 forks; issue activity unavailable in current metadata
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

58
GitHub stars
11
Freshness
Today
Install ready
Yes
License
AGPL-3.0
Review before install: Low GitHub adoption signal

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: mock-interview description: Runs a realistic mock interview in chat — builds an interviewer persona from the intel file, asks one question at a time with pressure follow-up chains, then scores the candidate on structured delivery, evidence density, role fit, follow-up resilience and recitation-smell, writing a report to the workspace. Use when the user wants practice, says 模拟面试 / 陪我练一下 / 当一回面试官 / 压力面 / mock interview, or when an interview is within 48 hours and the script is ready. Do NOT use for writing answers (interview-script) or reviewing a real past interview (debrief). license: AGPL-3.0 metadata: author: Yunyue Li version: "0.1.0" ---

# mock-interview · 模拟面试

在对话里扮演目标轮次的面试官,真实压力下检验逐字稿。产出 `jobs/<slug>/rounds/mock-N.md`。

## 准备

1. 读 `jobs/<slug>/intel.md`:用面试官档案建人格——背景决定追问方向(技术出身往机理挖、业务出身要数字、高管看判断),风格决定节奏(追问型 / 压力型 / 闲聊型)。没有面试官情报就问用户轮次类型,按 question-map 的轮次模块出题。 2. 读 `script.md`(知道他准备了什么,专挑边缘和缝隙问)、`intel.md` 考题预测。 3. 和用户确认:练全场(40-60 分钟节奏,10-15 题)还是专项(某模块 3-5 题);要不要压力面模式。

## 面试协议

- **一次只问一题**,等用户答完再下一题。用户的回答可以是打字或语音转文字。 - **像真面试官**:会打断(回答超 3 分钟时)、会顺着答案现场起追问、会换角度问同一件事(看回答前后是否一致、站不站得住)、偶尔不置可否直接跳下一题(测心态)。 - **追问链**走五步:数字出处 → 归因(扣掉自然增长了吗)→ 反事实 → 边界(换个场景还成立吗)→ 角色(你具体做了哪部分)。战绩题至少追两层。 - **中途不点评、不夸奖**。教练模式留到结束后。 - 压力面模式加三件套:质疑简历真实性、连续否定("这不就是执行吗")、沉默施压。开始前确认用户要不要。

## 评分与报告

结束后给报告并写入 `rounds/mock-N.md`(frontmatter:`type: mock / job / round / updated`):

### 评分维度(各 1-5 分 + 一句依据)

| 维度 | 看什么 | |------|--------| | 结构化表达 | 开口有没有骨架,分点是否清楚,有没有元叙述脚手架 | | 证据密度 | 数字和事实占比,空话占比;数字给不给得出出处 | | 岗位匹配 | 答的内容是否对准这个岗位真正在招的能力(对照 intel.md 匹配表),有没有答偏 | | 追问抗压 | 五步追问链能扛到第几层,被连续质疑时答案站不站得住、有没有自乱 | | 姿态与背诵感 | 有没有示弱/表忠心/自我标榜;像背稿还是像聊天 |

### 报告结构

1. 逐题记录:问题 → 回答要点 → 追问到第几层卡住 → 该题评分 2. 三个最该修的点(具体到某题某句怎么改) 3. 给 interview-script 的修订建议:哪些题答得不顺要重写、哪些追问没预案要补

逐字稿要改的,建议直接进 interview-script 迭代协议。

Technical details

Version
1.0.0
License
AGPL-3.0
Last updated
Aug 23, 2026
Published
Aug 23, 2026

Decision snapshot

Needs validation

57
Ready
Review
Stage

recent repository activity

Audit

Install review

Install and adoption review

78
Needs review
Security
86/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for mock-interview, ready for a manual X post.

Curator note
A practical pick for source-backed research:

mock-interview: Runs a realistic mock interview in chat — builds an interviewer persona from the intel file, asks one question at a time wi...

11 stars

https://www.openagentskill.com/skills/yunyueli-mock-interview?ref=x
Open X draft
Optional reply with install command
Listing + install path for mock-interview:
https://www.openagentskill.com/skills/yunyueli-mock-interview?ref=x

Install: npx skills add YunyueLi/greenroom --skill mock-interview

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
YunyueLi
Indexed by
OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

Claim this skill

Owner claim

Claim this skill listing

This Registry indexed listing is attributed to YunyueLi but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

Creator backlink kit

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Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.

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Author

Y

YunyueLi

@yunyueli

Platform fit

Health signals

GitHub stars
11
Quality score
31/100
Last GitHub push
Aug 23, 2026
Framework hints
Unknown
OpenAgentSkill views
0
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

67
  • GitHub adoption11 GitHub starsFIX
  • Stars/forks activity11 stars, 1 forks; issue activity unavailable in current metadataFIX
  • Recent maintenancePushed todayPASS
  • License clarityAGPL-3.0PASS
  • README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
  • Dependency/runtime riskno major dependency risk hints in public metadataPASS