Recruiter Assistant 🦞

This skill implements a high-bar recruitment workflow for technical hiring, specifically optimized for the Shenzhen market.

Workflows

1. Rigorous Resume Screening

Evaluate a candidate with a critical lens.

• Single: node scripts/screen_resume.js <path_to_resume> --lang <language> --yoe <years_of_experience>
• Batch: node scripts/batch_screen.js <folder_path> --threshold <score> --lang <language> --yoe <years_of_experience>
• Output Requirements:
  1. Strict Scoring: Adheres to the 0-100 rubric in references/hiring-criteria.md. High standards for "Senior" roles (must show architectural impact and expert AI usage).
  2. Detailed Analysis: Explicitly lists at least 3-4 hard technical strengths and significant weaknesses/gaps.
  3. Separate Reporting: Each candidate evaluation must be saved/written to its own document.
  4. Salary Benchmark: Compares the candidate's expected salary against Shenzhen market rates (Boss Zhipin 2026).
  5. HR Notification: High-scoring candidates (>= threshold) should be summarized and sent to HR via the message tool.

2. AI Proficiency Evaluation

Mandatory check for AI tool usage (Cursor, Copilot, LLM APIs). Lack of AI usage is considered a significant productivity gap.

3. Interview Preparation & Summary

• Questions: node scripts/generate_questions.js <input_json> (Focuses on the identified "Cons").
• Summarization: node scripts/summarize_interview.js <notes_file> (Uses the template in assets/report-template.md).

Market Benchmark (Shenzhen 2026)

Refer to references/hiring-criteria.md for the latest salary data and scoring rubrics.

Core Principles

• Critical Lens: Do not give high scores easily. High seniority requires evidence of architectural impact.
• Data-Driven: Benchmarks must align with the current Shenzhen tech market.
• AI-Forward: Efficiency through AI is a core requirement for a modern senior engineer.