interview-assessment

Dual-audience interview assessment and preparation skill for recruiters and candidates.

中文 README

Installation

Recommended:

npx skills add archlizheng/interview-assessment

Then restart Codex to pick up the new skill.

Manual Git install:

git clone https://github.com/archlizheng/interview-assessment.git "${CODEX_HOME:-$HOME/.codex}/skills/interview-assessment"

Update an existing manual install:

git -C "${CODEX_HOME:-$HOME/.codex}/skills/interview-assessment" pull

Uninstall:

rm -rf "${CODEX_HOME:-$HOME/.codex}/skills/interview-assessment"

Quick start

Recruiter mode:

Use interview-assessment to evaluate this candidate for recruiter audience.

jdText:
<paste the job description>

resumeText:
<paste the resume>

metadata:
- candidateName: <name>
- appliedRole: <role>
- company: <company>
- language: en

Candidate mode:

Use interview-assessment to help me prepare for this role.

jdText:
<paste the job description>

resumeText:
<paste my resume>

metadata:
- candidateName: <name>
- appliedRole: <role>
- language: en

What it does

This skill turns JD, resume, and optional interview transcript material into Markdown reports.

It supports two audiences:

• recruiter: candidate screening, interviewer preparation, and post-interview evaluation.
• candidate: role-fit self-assessment, interview preparation, answer strategy, and post-interview review.

Default output is Markdown. JSON is optional for automation and app integration.

Inputs

Required:

• jdText
• resumeText

Optional:

• interviewTranscriptText or interview notes
• metadata.candidateName
• metadata.appliedRole
• metadata.company
• audience: recruiter | candidate
• outputMode: markdown | json | both
• outputLanguage or metadata.language

Defaults:

• audience is inferred from the request.
• outputMode defaults to markdown.
• Output language is inferred from the user's request and input materials unless explicitly set.

Recruiter outputs

With JD + resume:

• 候选人初评报告.md
• 面试准备清单.md

With interview transcript or notes:

• 面试后综合评价报告.md

Recruiter reports may use hiring recommendation language: 通过 | 待定 | 拒绝.

English filenames:

• {candidateName}-candidate-pre-screening-report.md
• {candidateName}-interview-preparation-checklist.md
• {candidateName}-post-interview-evaluation-report.md

Candidate outputs

With JD + resume:

• 岗位匹配度自评报告.md
• 候选人面试准备清单.md

With interview transcript, notes, or interview experience:

• 面试后复盘与跟进建议.md

Candidate reports must not imply a guaranteed hiring outcome or use recruiter rejection language.

English filenames:

• {candidateName}-role-fit-self-assessment.md
• {candidateName}-candidate-interview-preparation-checklist.md
• {candidateName}-post-interview-review-and-follow-up.md

Language behavior

The skill localizes both report content and filenames.

Priority:

1. Explicit user instruction, outputLanguage, or metadata.language.
2. The language of the user's request.
3. The dominant language of the JD and resume.
4. zh-CN if still unclear.

Names, companies, role titles, and evidence quotes should remain in their original language unless translation is requested.

Optional JSON mode

Use outputMode: json or outputMode: both only when you need structured automation output.

The optional contract is documented in references/evaluationBundle.schema.md.

Examples

• examples/recruiter-input.md
• examples/candidate-input.md
• docs/usage-guide.zh-CN.md
• examples/candidate-output-sample.zh-CN.md

Responsible use

Hiring decisions should remain human-reviewed and evidence-based. Candidate-facing guidance is preparation support, not a guarantee of interview results. See docs/responsible-use.md.