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    AI Candidate Assessment Recruitment Suite

    1

    A structured, bias-aware recruitment suite for criteria extraction, candidate scoring, and interview preparation.

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    AI Candidate Assessment Recruitment Suite

    AI Candidate Assessment Recruitment Suite

    Example session with this skill installed

    Assess this resume against the Senior Backend Engineer JD I just uploaded. Use technical specialist weights. Generate a 60-minute interview guide.

    • Read your context and instructions
    • Compiled the ai candidate assessment

    I have completed the assessment for Candidate 1 against the Senior Backend Engineer role. The evaluation followed the Engineering/Technical Specialist weighting (50% Technical, 30% Experience, 20% Soft Skills).

    Candidate Assessment Report

    Role: Senior Backend Engineer | Candidate ID: Candidate 1 | Mode: Full Pipeline

    Part 1: Criteria Matrix

    IDCriterionCategoryTypeMax PointsEvidence to Look For
    T1Distributed Systems DesignTechnicalMust-have20Experience with microservices, scaling, and message queues.
    T2Go / Python ProficiencyTechnicalMust-have15Direct application in production environments.
    T3API Design (gRPC/REST)TechnicalMust-have15Documentation or ownership of public/internal APIs.
    E15+ Years Backend ExpExperienceMust-have20Sustained tenure in high-scale backend roles.
    S1Technical MentorshipSoft SkillsNice-to-have10Evidence of leading code reviews or junior dev growth.

    Part 2: Resume Analysis & Fit Score

    Candidate Snapshot: 8 years of experience in backend development. Most recently a Lead Engineer at a fintech startup. Strong focus on Kubernetes and Go.

    Fit Score: 84/100 (Strong Match)

    • Technical: 42/50
    • Experience: 28/30
    • Soft Skills: 14/20

    Confidence: High. Resume includes specific outcomes and toolsets for all roles.

    Points to Verify:

    FlagTypeEvidenceScore ImpactHow to Verify
    TenureShort Tenure10 months at AlphaCorpNoneAsk about transition to current role.

    Part 3: Interview Guide (60 Minutes)

    Focus Areas:

    1. T1 (Distributed Systems): Confirm depth in message queue failure modes.
    2. S1 (Mentorship): Verify specific examples of growing junior talent.

    Technical Question (T1): "Describe a time you had to troubleshoot a bottleneck in a distributed system. What metrics did you use?"

    • Strong Signal: Mentions specific tools (Prometheus, Jaeger) and architectural changes.
    • Weak Signal: Vague mentions of 'slowness' without root cause analysis.

    Part 4: Executive Decision Summary

    Recommendation: Strong Hire
    Rule Applied: Fit Score > 80 and all must-haves rated 3+.

    Assessment: The candidate demonstrates significant ownership of backend infrastructure and meets all technical requirements. The brief tenure at AlphaCorp is the only point requiring clarification.

    Next steps

    1. Review the AlphaCorp tenure during the verification segment.
    2. Conduct the technical deep-dive using the T1 scenario provided.
    3. Compare against Candidate 2 once their assessment is complete.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Hiring managers and recruiters often struggle with inconsistent candidate screening and unconscious bias. This skill transforms job descriptions and resumes into evidence-based assessments, ensuring every applicant is measured against the same weighted rubric.

    What it does Criteria extraction parses job descriptions to identify must-have skills, nice-to-haves, and core responsibilities. Evidence-based scoring rates candidates on a 0 to 4 scale using only demonstrated outcomes and stated experience. Bias mitigation automatically excludes protected characteristics like age, gender, and graduation years from the evaluation process. Tailored interview guides generates specific technical and behavioral questions based on a candidate's individual gaps and strengths. Executive summaries provides a standardized hire or pass recommendation with a clear audit trail for human reviewers. How it works Provide documents by pasting a job description and one or more resumes into the chat. Define weights or allow the skill to apply a preset category weighting based on the role family. Review the matrix to confirm the extracted criteria and scoring logic align with your hiring goals. Analyze reports including the fit score, verification flags for gaps or short tenures, and the structured interview guide. Frameworks & tools

    This skill operates as a standalone analytical engine. It is compatible with any LLM capable of processing long-form text and PDF/DOCX attachments.

    Why this beats prompting it yourself

    Manual prompting often leads to hallucinated criteria or inconsistent scoring between candidates. This skill enforces a strict, multi-module pipeline that ensures every candidate is evaluated against the exact same rubric, producing defensible and audit-ready hiring memos.

    Use cases Screening batches of applicants for a single role to identify the top 10% for interviews. Generating interview plans for a specific candidate to probe identified technical gaps. Standardizing hiring across a distributed team of interviewers using a shared rubric. Auditing job descriptions for biased language or unnecessary credential requirements. Known limitations

    This skill only evaluates supplied documents and cannot verify credentials or perform background checks. It requires a job description to function and cannot assess live soft skills.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for HR & Recruiting.

    Requires a job description or explicit criteria to score. Only assesses supplied text; cannot verify credentials or browse the web. High-volume batches may require context window monitoring.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Listed1 month ago

    What's inside

    Frequently Asked Questions

    The suite uses a multi-module pipeline to strip protected characteristics—such as name, gender-coded language, and graduation years—focusing exclusively on demonstrated outcomes and skill-based evidence to ensure objective scoring.

    This skill is designed as a standalone analytical engine compatible with any major LLM that supports long-form text and document attachments (PDF or DOCX), including ChatGPT, Claude, and Gemini.

    You will receive the full system architecture for the suite, including modules for criteria extraction, the 0-4 scoring logic, and the interview guide generator, along with a setup guide for immediate deployment.

    Unlike manual prompts, this skill enforces a structured, weighted rubric across all candidates, eliminating the 'drift' or hallucinated criteria that often occur when an AI evaluates resumes one by one without a locked framework.

    No complex installation is required; simply input your job description and resumes into your preferred AI interface, and the suite’s logic will guide you through the extraction and matrix review steps.

    The suite is excellent for identifying 'red flags' like short tenures or missing technical requirements, but it cannot perform external background checks or verify the legal truth of the documents provided.

    New in HR & Recruiting