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AI Candidate Assessment Recruitment Suite
A structured, bias-aware recruitment suite for criteria extraction, candidate scoring, and interview preparation.
$12
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
- Generated the document
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
| ID | Criterion | Category | Type | Max Points | Evidence to Look For |
|---|---|---|---|---|---|
| T1 | Distributed Systems Design | Technical | Must-have | 20 | Experience with microservices, scaling, and message queues. |
| T2 | Go / Python Proficiency | Technical | Must-have | 15 | Direct application in production environments. |
| T3 | API Design (gRPC/REST) | Technical | Must-have | 15 | Documentation or ownership of public/internal APIs. |
| E1 | 5+ Years Backend Exp | Experience | Must-have | 20 | Sustained tenure in high-scale backend roles. |
| S1 | Technical Mentorship | Soft Skills | Nice-to-have | 10 | Evidence 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:
| Flag | Type | Evidence | Score Impact | How to Verify |
|---|---|---|---|---|
| Tenure | Short Tenure | 10 months at AlphaCorp | None | Ask about transition to current role. |
Part 3: Interview Guide (60 Minutes)
Focus Areas:
- T1 (Distributed Systems): Confirm depth in message queue failure modes.
- 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
- Review the AlphaCorp tenure during the verification segment.
- Conduct the technical deep-dive using the T1 scenario provided.
- Compare against Candidate 2 once their assessment is complete.
ai-candidate-assessment-recruitment-suit.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
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.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
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- 3
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