Applicant Screening

応募者選考

職務要件に照らして求人応募を選考し、優秀な候補者を効率よく特定します。

概要

このスキルでは、次のことができます。

• 職務要件に照らして履歴書を評価する
• 候補者を一貫した基準で採点する
• 必須要件と、あると望ましい要件を特定する
• 懸念される可能性のある点に注意を促す
• 面接対象者の順位を付ける

使用方法

1名の候補者

"Screen this resume against our [Job Title] requirements"
"Evaluate this application for the [Position] role"

複数名の選考

"Screen these 10 applications for the Senior Developer position"
"Rank these candidates based on our requirements"

評価基準を指定する場合

"Screen for: 5+ years Python, AWS experience required, ML nice-to-have"

選考のフレームワーク

要件マトリックス

## Job Requirements: [Position]

### Must-Have (Required)
| Requirement | Weight | Criteria |
|-------------|--------|----------|
| [Skill 1] | 20% | [X] years experience |
| [Skill 2] | 15% | [Certification/level] |
| [Education] | 10% | [Degree type] |
| [Experience] | 25% | [Industry/role type] |

### Nice-to-Have (Preferred)
| Requirement | Bonus | Criteria |
|-------------|-------|----------|
| [Skill 3] | +5pts | [Description] |
| [Skill 4] | +5pts | [Description] |
| [Trait] | +3pts | [Indicator] |

### Disqualifiers
- [ ] No work authorization
- [ ] Below minimum experience
- [ ] Missing required certification
- [ ] Salary expectation mismatch

出力形式

個別選考レポート

# Candidate Screening: [Name]

## Quick Summary
| Attribute | Value |
|-----------|-------|
| **Position** | [Job Title] |
| **Score** | [X]/100 |
| **Recommendation** | 🟢 Interview / 🟡 Maybe / 🔴 Pass |

## Candidate Profile
- **Name**: [Full Name]
- **Location**: [City, State]
- **Current Role**: [Title] at [Company]
- **Total Experience**: [X] years
- **Education**: [Degree, School]

## Requirements Match

### Must-Have Requirements
| Requirement | Met? | Evidence | Score |
|-------------|------|----------|-------|
| [5+ years Python] | ✅ | 7 years at 2 companies | 20/20 |
| [AWS experience] | ✅ | AWS Certified, 3 years | 15/15 |
| [Bachelor's CS] | ✅ | BS Computer Science, MIT | 10/10 |
| [Team lead exp] | ⚠️ | Led 2-person team | 5/10 |

**Must-Have Score**: [X]/[Total]

### Nice-to-Have
| Requirement | Met? | Evidence | Bonus |
|-------------|------|----------|-------|
| [ML experience] | ✅ | Built recommendation system | +5 |
| [Startup exp] | ✅ | 2 early-stage startups | +5 |
| [Open source] | ❌ | Not mentioned | 0 |

**Nice-to-Have Bonus**: +[X] points

## Strengths 💪
1. [Strength 1 with evidence]
2. [Strength 2 with evidence]
3. [Strength 3 with evidence]

## Concerns ⚠️
1. [Concern 1 - question to ask in interview]
2. [Concern 2 - what to verify]

## Red Flags 🚩
- [If any - employment gaps, inconsistencies, etc.]

## Interview Questions
Based on this candidate's profile, consider asking:
1. [Question about specific experience]
2. [Question about concern area]
3. [Question about growth potential]

## Overall Assessment
[2-3 sentence summary of fit]

**Final Score**: [X]/100
**Recommendation**: [Interview / Phone Screen / Pass]
**Priority**: [High / Medium / Low]

複数名の順位付けレポート

# Applicant Ranking: [Position]

**Date**: [Date]
**Total Applications**: [X]
**Reviewed**: [X]

## Summary
| Category | Count | % |
|----------|-------|---|
| 🟢 Strong Interview | [X] | [%] |
| 🟡 Phone Screen | [X] | [%] |
| 🔵 Maybe/Hold | [X] | [%] |
| 🔴 Not a Fit | [X] | [%] |

## Top Candidates

### 🥇 Tier 1: Strong Interview (Score 80+)

| Rank | Name | Score | Key Strengths | Concerns |
|------|------|-------|---------------|----------|
| 1 | [Name] | 92 | [Strengths] | [Concerns] |
| 2 | [Name] | 88 | [Strengths] | [Concerns] |
| 3 | [Name] | 85 | [Strengths] | [Concerns] |

### 🥈 Tier 2: Phone Screen (Score 65-79)

| Rank | Name | Score | Key Strengths | Gap to Address |
|------|------|-------|---------------|----------------|
| 4 | [Name] | 75 | [Strengths] | [Gap] |
| 5 | [Name] | 72 | [Strengths] | [Gap] |

### 🥉 Tier 3: Maybe/Hold (Score 50-64)

| Name | Score | Reason for Hold |
|------|-------|-----------------|
| [Name] | 58 | [Reason] |

### ❌ Not Proceeding (Score <50)

| Name | Score | Primary Reason |
|------|-------|----------------|
| [Name] | 45 | Missing required [X] |
| [Name] | 38 | Below minimum experience |

## Insights

### Applicant Pool Quality
[Assessment of overall pool quality]

### Common Strengths
- [Frequently seen strength]
- [Frequently seen strength]

### Common Gaps
- [What most candidates lack]
- [Skill shortage in pool]

### Recommendations
1. [Action for top candidates]
2. [Suggestion for sourcing if pool weak]

採点基準

経験年数の採点

年数	初級	中級	シニア	リード
0-1	10/10	3/10	0/10	0/10
2-3	8/10	7/10	3/10	0/10
4-5	5/10	10/10	7/10	3/10
6-8	3/10	8/10	10/10	7/10
9+	0/10	5/10	10/10	10/10

学歴の採点

学歴	技術職	非技術職
博士号	10/10	8/10
修士号	9/10	9/10
学士号	8/10	10/10
準学士号	5/10	7/10
ブートキャンプ	6/10	該当なし
独学	4/10	該当なし

ベストプラクティス

公平な選考

• 職務に関連する基準のみに注目する
• 保護対象の属性を考慮しない
• 一貫した基準で採点する
• 判断内容を記録する
• 多様な経歴を考慮する

バイアスへの認識

• 名前・性別によるバイアス：資格や能力に注目する
• 類似性バイアス：多様な面接パネルを構成する
• 確証バイアス：直感で判断する前に採点する
• ハロー効果：各基準を個別に評価する

法的な考慮事項

• 職務に関連する基準のみを使用する
• 基準を一貫して適用する
• 選考記録を保管する
• 人事部にプロセスを確認してもらう
• 不利益な影響を考慮する

制限事項

• 職歴を検証することはできません
• 従来とは異なる経歴に関する背景を見落とす場合があります
• 採点はあくまで参考であり、絶対的なものではありません
• 文化的適合性やソフトスキルを十分に評価することはできません
• 最終判断には人間の判断が不可欠です