HireMate — AI招聘助手
v1.0.0AI recruiting assistant for generating job descriptions, resume screening criteria, interview questions, candidate scoring, match analysis, salary reports, a...
HireMate — AI Recruiting Assistant
Complete recruiting toolkit: generate JDs, screen resumes, create interview questions, score candidates, analyze matches, benchmark salaries, and evaluate interviews.
Free Features
Generate Job Description
python3 <skill_dir>/scripts/generate_jd.py \
--role <role_template> --company <name> --seniority <level> \
--location <loc> --language <lang> --framework <fw> \
--industry <ind> --years <n> [--output file.md]
Roles: software_engineer, product_manager, data_scientist, ux_designer, marketing_manager, sales_representative
Generate Screening Criteria
python3 <skill_dir>/scripts/generate_screening_criteria.py \
--role <role> --years <n> --languages "Py,Go" \
--frameworks "Django" --industry <ind> [--format json]
Generate Interview Questions
python3 <skill_dir>/scripts/generate_interview_questions.py \
--role <role> --level <junior|mid|senior|all> \
--num-tech 5 --num-behavioral 3 [--seed <n> for reproducibility]
Premium Features
Score Resume
python3 <skill_dir>/scripts/score_resume.py \
--resume "<text>" --keywords skill1 skill2 skill3 \
--min-years 3 --education <bachelors|masters|phd>
Resume can be text or @file_path. Returns weighted score (keyword 40%, experience 25%, education 15%, tech skills 20%).
Candidate-Job Match Analysis
python3 <skill_dir>/scripts/match_candidate.py \
--candidate-skills a b c --candidate-years 5 \
--candidate-edu bachelors --candidate-location "SF" \
--job-skills a b d --job-min-years 3 \
--job-role software_engineer --job-seniority mid
Returns skill overlap, experience gap, education match, and salary context.
Salary Market Report
python3 <skill_dir>/scripts/salary_report.py \
--role <role> --region <us|europe|uk|asia_pacific> \
--seniority <junior|mid|senior|staff>
Includes regional comparisons and market notes.
Interview Evaluation Report
python3 <skill_dir>/scripts/interview_evaluation.py \
--candidate "Name" --role "Role" \
--technical 85 --problem-solving 80 --communication 75 \
--cultural-fit 90 --experience 80 --learning 85 \
--strengths "Strong X" "Great Y" \
--concerns "Needs Z"
Weighted scoring: technical 30%, problem-solving 20%, communication 15%, cultural fit 15%, experience 10%, learning 10%.
Reference Data
references/jd_templates.json— JD templates for 6 role typesreferences/interview_questions_db.json— 50+ questions across 6 categoriesreferences/salary_data.json— Global salary benchmarks (US, EU, UK, APAC)
Usage Notes
- All scripts support
--format markdown(default) and--format json - Use
--output file.mdto save to file instead of stdout - Use
--seedfor reproducible interview question sets - Salary data should be updated quarterly with latest market data
- For roles not in templates, use the AI to generate custom JDs based on the template structure
