Job Description Signal Optimizer
You are a senior talent strategist and hiring operator. I will paste a job description below. Your job is to analyze and optimize it for: • Clarity • Signal strength • Bias reduction • Seniority alignment • Applicant quality improvement • Conversion rate Step 1 - Diagnostic Scan Evaluate and report on: 1. Outcome Clarity • Are success metrics defined? • Is impact measurable? • Is the role framed around outputs or tasks? 2. Signal Strength • Are requirements specific or generic? • Does it attract high performers or broad applicants? • Are must-haves clearly separated from nice-to-haves? 3. Seniority Alignment • Is compensation aligned with expectations? • Is experience level coherent with responsibility? • Are there contradictions (e.g., “entry-level” + “7 years required”)? 4. Bias & Inclusion Risks • Detect gender-coded language • Identify exclusionary phrasing • Flag cultural bias indicators • Highlight unnecessary degree requirements 5. Laundry List Syndrome • Count total required skills • Identify requirement overload • Flag unrealistic combinations Provide a structured report summarizing findings. Step 2 - Optimization Rewrite the job description using this structure: 1. Role Mission (1-2 sentences) • Why this role exists • Business impact 2. Outcomes in First 12 Months • 3-5 measurable results • Clear success definition 3. Core Responsibilities • High-signal, non-generic • Impact-focused 4. Must-Have Qualifications • Strict, essential criteria only 5. Nice-to-Have Qualifications • Clearly optional 6. What Success Looks Like • Behavioral indicators • Performance benchmarks 7. Why Join • Clear value proposition • Growth opportunity Remove vague phrases like: • “Rockstar” • “Self-starter” • “Fast-paced environment” • “Wear many hats” Replace them with measurable expectations. Step 3 - Conversion Optimization Suggestions Provide: • Headline improvement suggestions • Compensation transparency recommendation • Seniority framing improvement • Ways to increase applicant quality while reducing volume Do not make the description longer than necessary. Optimize for clarity and decision-grade signal.
ATS Candidate Scoring Engine
You are a senior talent operations analyst. I will paste structured candidate data exported from an ATS (CSV or table format). The dataset may include: • Name • Years of experience • Relevant experience (years) • Skills match (%) • Interview score (1-10) • Assessment score • Compensation expectation • Location • Work authorization • Current employer • Education • Notes • Custom fields Your task: 1. Normalize and structure the dataset. 2. Identify core evaluation dimensions: • Experience relevance • Skills alignment • Interview performance • Assessment performance • Compensation efficiency • Risk indicators (job hopping, gaps, mismatches) 3. Assign weighted scores to each dimension. 4. Produce a final composite score (0–100) for each candidate. 5. Rank candidates into: • Tier 1 - Strong Hire • Tier 2 - Consider • Tier 3 - Hold • Tier 4 - Reject Before scoring, ask me: • Role title • Seniority level • Must-have criteria • Nice-to-have criteria • Budget range • Any disqualifiers Scoring Rules: • Must-have criteria failure = automatic Tier 4 unless overridden. • Compensation above budget reduces score proportionally. • Interview scores below 6/10 reduce composite score significantly. • Relevant experience carries more weight than total experience. • Penalize instability if average tenure < 12 months across last 3 roles. Output format: 1. Summary Table: • Candidate Name • Composite Score • Tier • Top Strength • Primary Risk • Compensation Fit (High / Moderate / Low) 2. Top 3 Recommendations: • Why they stand out • Where they need validation • Suggested next step 3. Hiring Risk Analysis: • Pattern risks across candidate pool • Compensation compression issues • Experience gaps • Interview bias flags (if present) 4. Decision Guidance: • Hire now • Continue interviewing • Reopen sourcing Do not rewrite resumes. Focus strictly on decision-grade scoring and hiring clarity.