Interview Assessment

• audience: recruiter: for HR, hiring managers, and interviewers who need candidate evaluation, interview planning, and post-interview recommendations.
• audience: candidate: for candidates who need role-fit self-assessment, interview preparation, resume or portfolio evidence improvement, and post-interview review.

Default to Markdown-first delivery. Use outputMode: json only when the user explicitly needs automation, ATS integration, app ingestion, or structured validation. Use outputMode: both when the user asks for both human-readable reports and a machine-readable evaluationBundle.

• If the user asks to evaluate a candidate, screen resumes, prepare interviewer questions, or make a hiring recommendation, use audience: recruiter.
• If the user says "I", asks to prepare for an interview, improve resume match, rehearse answers, or review their own interview, use audience: candidate.
• Use outputMode: markdown by default.

• jdText
• resumeText

Optional fields:

• interviewTranscriptText or interview experience notes
• interviewerNotes
• metadata such as candidate name, role, company, round, date, and target language
• audience: recruiter | candidate
• outputMode: markdown | json | both
• outputLanguage or metadata.language, such as zh-CN or en

If jdText or resumeText is missing, do not score or make a conclusion. Return an insufficientEvidence section that asks only for the missing material.

Language priority:

1. Explicit user instruction, outputLanguage, or metadata.language.
2. The language of the user's request.
3. If the request is mixed, use the dominant language of jdText and resumeText.
4. If still unclear, default to zh-CN.

Rules:

• Write all Markdown report content in the inferred output language.
• Localize section headings while preserving the required template structure and meaning.
• Localize filenames to the same output language.
• Preserve candidate names, company names, role names, product names, and quoted evidence in their original language unless translation is necessary for readability.
• If input materials are in one language but the user asks for another language, write analysis in the requested language and keep evidence quotes in the original language with a short translated explanation when helpful.

1. Extract objective evidence snippets.
2. Evaluate professional capability, soft qualities, personal style, motivation, and expectation alignment.
3. Output pending concerns and next-step recommendations.
4. Use audience-specific framing:
   • Recruiter: final hiring recommendation and process next step.
   • Candidate: interview performance review, follow-up strategy, and improvement plan.

For audience: recruiter, use recruiter-report-templates.md:

• {候选人姓名}-候选人初评报告.md
• {候选人姓名}-面试准备清单.md
• {候选人姓名}-面试后综合评价报告.md only when Stage 3 is produced

For audience: candidate, use candidate-report-templates.md:

• {候选人姓名}-岗位匹配度自评报告.md
• {候选人姓名}-候选人面试准备清单.md
• {候选人姓名}-面试后复盘与跟进建议.md only when Stage 3 is produced

For English output, use localized filenames:

• Recruiter:
  • {candidateName}-candidate-pre-screening-report.md
  • {candidateName}-interview-preparation-checklist.md
  • {candidateName}-post-interview-evaluation-report.md
• Candidate:
  • {candidateName}-role-fit-self-assessment.md
  • {candidateName}-candidate-interview-preparation-checklist.md
  • {candidateName}-post-interview-review-and-follow-up.md

For other languages, translate the filename suffix naturally and keep the candidate name unchanged.

Default directories:

• Recruiter: {workspaceRoot}/候选人评估报告/
• Candidate: {workspaceRoot}/候选人面试准备/

Candidate name:

• Use metadata.candidateName if present.
• Otherwise infer from the resume header only when explicit.
• If still unknown, use candidateId and ask for the name in follow-up; do not guess.

Rules:

• In json mode, do not write Markdown files unless asked.
• In both mode, Markdown and JSON must contain the same scores, conclusions, risks, recommendations, and evidence.
• JSON is an integration format, not the default HR/candidate deliverable.

Suggested role split:

• Role A: professional capability
• Role B: soft qualities and communication
• Role C: motivation, culture fit, and risk control

Aggregation:

1. Each role proposes scores, evidence, and concerns.
2. The orchestrator resolves conflicts by evidence quality.
3. Output the same audience-specific Markdown templates.
4. If JSON is requested, include agentVotes and roundtableSummary without removing base fields.

If a requested model is unavailable, use a single available model while preserving the role logic.