AI CV Screener Research Guide

📋 BASIC - Quick Pass/Fail Screening

Best for: High-volume initial screening, junior roles, clear-cut requirements.

What it does:

• Checks if candidate meets absolute minimum requirements (education, years of experience, critical skills).
• Simple pass/fail assessment - no nuance.
• Lists only critical gaps (deal-breakers).

Example JSON Output:

A minimal JSON object is returned, focusing only on the pass/fail criteria.

{
  "qualified": false,
  "score": 0.2,
  ...
  "gaps": [
    {
      "gap": "Fails to meet minimum 5 years of experience.",
      "critical_to_role": true,
      "compensated_by_other_strengths": "Not compensated"
    }
  ],
  "summary": "Candidate is unqualified...",
  "recommendation": "Reject"
}
                    

🎯 MODERATE - Balanced Assessment (Default)

Best for: Most hiring scenarios, mid-level roles, balanced decision-making.

What it does:

• Assesses minimum requirements + overall fit quality.
• Evaluates skill alignment and depth.
• Lists ALL gaps (critical + non-critical) with impact assessment.
• Explains how other strengths compensate for gaps.

Example JSON Output:

The JSON now includes strengths and a more detailed gaps array.

{
  "qualified": true,
  "score": 0.78,
  "strengths": [
    "Meets 5+ years experience requirement.",
    "Strong evidence of leading teams of 3-5 engineers."
  ],
  "gaps": [
    {
      "gap": "Lacks experience with Kubernetes (required skill).",
      "critical_to_role": true,
      "compensated_by_other_strengths": "Not compensated - significant concern."
    },
    {
      "gap": "No PMP certification (preferred skill).",
      "critical_to_role": false,
      "compensated_by_other_strengths": "10+ years leading projects demonstrates practical expertise."
    }
  ],
  "summary": "Candidate is qualified and has strong leadership skills...",
  "recommendation": "Interview"
}
                    

🔍 COMPREHENSIVE - Weighted Multi-Dimensional Analysis

Best for: Senior roles, leadership positions, strategic hires.

What it does:

• Everything in Moderate, PLUS:
• Weighted scoring framework: Skills (40%) + Responsibilities (35%) + Education (20%) + Fit (5%).
• Deep responsibility_alignment analysis (3-5 key responsibility areas evaluated individually).
• Assesses scope, scale, and complexity of past experience vs. job requirements.

Example JSON Output:

The JSON now features the critical responsibility_alignment object with individual scores.

{
  "qualified": true,
  "score": 0.82,
  ...
  "responsibility_alignment": {
    "key_areas": [
      {
        "area": "Network Security Infrastructure Management",
        "assessment": "Candidate has 5 years managing network security... but no direct experience with the required Cisco ASA platform.",
        "match_score": 0.72
      },
      {
        "area": "Vendor and Stakeholder Communication",
        "assessment": "Extensive evidence of effective communication with non-technical stakeholders...",
        "match_score": 0.93
      }
    ],
    "overall_score": 0.825
  },
  ...
  "recommendation": "Recommend with Reservations"
}
                    

🔬 EXTENDED - Deep-Dive with Mandatory Tables

Best for: Executive roles, highly specialized positions, forensic evaluation.

What it does:

• Everything in Comprehensive, PLUS:
• component_scores: A full breakdown of all 4 weighted areas.
• skills_assessment: Tables of skills met, missing, preferred, and transferable.
• red_flags: Analysis of employment gaps, job hopping, etc.
• growth_trajectory: Analysis of the candidate's career pattern.
• interview_focus_areas: Specific questions to probe gaps and concerns.

Example JSON Output:

This is the most detailed JSON, including all analytical components for the "data-aware" UI.

{
  "qualified": true,
  "score": 0.84,
  "component_scores": { ... },
  "skills_assessment": {
    "required_skills_met": [ ... ],
    "required_skills_missing": ["Azure", "GCP"],
    ...
  },
  "qa_analysis_report": {
    "overall_assessment": "The Q&A responses are strong... (etc.)",
    "key_strengths_revealed": ["Confirmed Laravel expertise not obvious on CV."],
    "concerns_raised": ["Lacks depth on specific CI/CD tools mentioned."]
  },
  "red_flags": ["Frequent job changes (2015-2018)"],
  "growth_trajectory": "Candidate shows a clear ascending pattern...",
  "recommendation_rationale": "A strong architect with proven leadership...",
  "interview_focus_areas": [
    "Probe on multi-cloud knowledge.",
    "Discuss reasons for job changes prior to 2018."
  ]
}
                    

🚀 ADVANCED - Forensic Analysis with Recency & Currency

Best for: Fast-evolving fields (tech, AI, cybersecurity), roles requiring cutting-edge knowledge.

What it does:

• Everything in Extended, PLUS:
• **Recency & Currency Assessment**: The AI actively searches for *when* skills were used.
• The skills_assessment now includes outdated_skills (e.g., "Hadoop, last used 2020").
• Key skills get a recency_note (e.g., "Certification expired 2022").
• Gaps analysis will now include recency as a category.

Example JSON Output:

The JSON output is identical in structure to Extended, but the *content* is now focused on recency.

{
  /* ... (Same fields as Extended) ... */

  "skills_assessment": {
    "required_skills_met": [
      {
        "skill": "PyTorch",
        "proficiency_evidence": "Used extensively at DataCorp (2021-2024).",
        "years_experience": 4,
        "recency_note": "Current and active. Most recent usage within last 3 months."
      }
    ],
    "outdated_skills": [
      "Hadoop administration (last used 2020, technology declining)",
      "Cloudera CDH (certification expired 2022)"
    ]
  },
  "gaps": [
    {
      "gap": "Experience with Hadoop/Spark is dated (last used 4+ years ago).",
      "category": "recency",
      ...
    }
  ]
}
                    

Dynamic Ad-Hoc Analysis

Use Case: You have a specific, one-off question not covered by the standard prompt.

How it works:

Simply add your question to any prompt (Basic, Moderate, etc.) in the "Advanced Editor".

Your Custom Instruction:

"Evaluate the candidate... (all other instructions) ...
Produce an assessment statement about the candidate's leadership skills."

Resulting JSON (The AI adds the additional_findings block):

{
  "qualified": true,
  "score": 0.78,
  ...
  "additional_findings": [
    {
      "title": "Leadership Skills Assessment",
      "finding": "The candidate demonstrates strong leadership potential. They managed a team of 3 developers at their previous role and led a successful microservices migration project. This aligns well with the 'team lead' responsibilities of the role."
    }
  ]
}