# Interview Assessment

> Use when evaluating JD/resume/interview materials for recruiters, or when helping candidates assess role fit, prepare interviews, improve evidence, and review interview performance.

- Skill: `archlizheng/interview-assessment` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add archlizheng/interview-assessment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/archlizheng/interview-assessment/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: archlizheng (https://skillmd.com/u/archlizheng)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/archlizheng/interview-assessment

---


<objective>
Support evidence-based interview assessment for two audiences:

- `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`.
</objective>

<intent_detection>
Infer defaults when the user does not specify them:

- 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.
</intent_detection>

<required_inputs>
Minimum required fields:

- `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.
</required_inputs>

<language_and_filename_policy>
Infer output language unless the user explicitly sets `outputLanguage` or `metadata.language`.

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.
</language_and_filename_policy>

<workflow>
<stage_1_fit_assessment>
1. Extract must-have skills, role context, seniority, domain expectations, and success signals from the JD.
2. Map resume evidence to the JD.
3. Score in 0-100:
   - workExperience
   - professionalCapability
   - softSkills
4. Compute weighted score using `scoring-rubric.md`.
5. For each dimension, include:
   - score
   - 1-3 sentence rationale
   - 1-2 grounded evidence quotes from the JD/resume/interview material
6. Apply audience-specific language:
   - Recruiter: output `通过 | 待定 | 拒绝` and explain hiring risk.
   - Candidate: output fit level and preparation priority; do not use hiring-decision wording.
</stage_1_fit_assessment>

<stage_2_preparation>
1. Identify uncertainty zones and missing evidence.
2. Produce focus areas with `high | medium | low` priority.
3. Generate targeted questions and answer strategy:
   - behavioral questions
   - technical/domain questions
   - motivation and expectation questions
4. For recruiter output, include target competency and follow-up hints.
5. For candidate output, include answer angle, evidence to prepare, and weak spots to repair.
</stage_2_preparation>

<stage_3_post_interview_review>
Run this stage only when `interviewTranscriptText`, interview notes, or interview experience is provided.

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.
</stage_3_post_interview_review>
</workflow>

<output_contract>
<markdown_mode>
Markdown mode is the default. Write Markdown files unless the user asks for chat-only or no files.

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.
</markdown_mode>

<json_mode>
When the user explicitly requests `outputMode: json` or `outputMode: both`, return a complete `evaluationBundle` using `references/evaluationBundle.schema.md` as the optional automation contract.

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.
</json_mode>
</output_contract>

<audience_rules>
<recruiter_mode>
- Keep language concise, evidence-based, and suitable for HR or interview panels.
- Use decision vocabulary: `通过 | 待定 | 拒绝`.
- Include risks, must-verify items, next-round suggestions, and evidence quotes.
- For high-impact decisions (`通过` or `拒绝`), include at least 2 grounded evidence quotes.
- Do not invent facts not found in the input.
</recruiter_mode>

<candidate_mode>
- Keep language practical, coaching-oriented, and non-deterministic.
- Do not use phrases such as "建议拒绝该候选人" or imply a guaranteed hiring outcome.
- Use fit and preparation vocabulary:
  - 匹配度判断
  - 优势证据
  - 风险短板
  - 回答策略
  - 简历与作品集补强建议
  - 面试后跟进建议
- Scores are preparation references, not predictions of hiring results.
</candidate_mode>
</audience_rules>

<multi_agent_mode>
Use only when the user asks for multi-agent or panel-style assessment.

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.
</multi_agent_mode>

<responsible_use>
Follow `docs/responsible-use.md` for fairness, evidence grounding, and sensitive decision handling. Never invent qualifications, protected-class signals, interview performance, or hiring conclusions beyond the provided evidence.
</responsible_use>