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#

bash

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#

bash

python3 <skill_dir>/scripts/generate_screening_criteria.py \
  --role <role> --years <n> --languages "Py,Go" \
  --frameworks "Django" --industry <ind> [--format json]

Generate Interview Questions#

bash

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#

bash

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#

bash

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#

bash

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#

bash

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 types
• references/interview_questions_db.json — 50+ questions across 6 categories
• references/salary_data.json — Global salary benchmarks (US, EU, UK, APAC)

Usage Notes#

• All scripts support --format markdown (default) and --format json
• Use --output file.md to save to file instead of stdout
• Use --seed for 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