AI Skill Report Card

Recruiting Engineering Talent

B+78·Jan 12, 2026

Engineering Talent Recruiting

Boolean search for senior Python engineers:

(senior OR lead) AND (Python OR Django OR Flask) AND (startup OR "early stage") -recruiter -agency

MATCH framework evaluation:

  • Motivation: Why this role/company?
  • Aptitude: Problem-solving ability
  • Technical depth: Skills match
  • Culture fit: Startup environment alignment
  • Hunger: Growth mindset and drive
Recommendation
Add concrete input/output examples for GitHub evaluation and technical depth assessment beyond just the motivation examples

Sourcing Phase:

  1. Create boolean searches targeting specific tech stacks
  2. Review GitHub contributions for code quality and activity
  3. Check LinkedIn for startup experience and career progression
  4. Build initial candidate list with notes

Screening Process:

  • Initial 15-min phone screen using MATCH framework
  • Technical depth assessment (can discuss their projects?)
  • Motivation and hunger evaluation
  • Culture fit for startup environment
  • Schedule next steps if positive

Interview Loop Management:

  • Prep candidate: what to expect, who they'll meet, technical focus areas
  • Coordinate scheduling with hiring team
  • Follow up after each round
  • Debrief with hiring managers: strengths, concerns, calibration
  • Guide candidate decision-making process
Recommendation
Include specific templates or frameworks for the 15-minute phone screen structure and debrief formats

Example 1: Motivation Assessment Question: "What drew you to apply for this role?" Strong answer: "I want to build something from scratch and see direct impact of my work. Your product solves a real problem I've experienced." Weak answer: "Looking for better compensation and title bump."

Example 2: Soft Close Conversation "Based on our conversations, I'm hearing that you value X, Y, and Z. This role offers X and Y strongly, but Z might be limited in year one. How does that sit with you? What would need to change for this to feel like your dream role?"

Recommendation
Expand the boolean search examples to cover more tech stacks and include platform-specific search syntax (LinkedIn, GitHub, etc.)
  • Lead with curiosity, not selling. Ask what they're optimizing for in their next role
  • Prep candidates thoroughly. Share interviewer backgrounds, technical focus areas, and company context
  • Calibrate with hiring managers. Understand their "must-haves" vs "nice-to-haves"
  • Use GitHub as a signal. Recent commits and project quality matter more than follower count
  • Focus on startup alignment. Early-stage requires comfort with ambiguity and wearing multiple hats
  • Hard selling before understanding fit. Let candidates self-select out if it's not right
  • Skipping technical depth assessment. Even senior engineers vary widely in actual capability
  • Under-prepping candidates. Poor interview experience reflects on you and the company
  • Ignoring culture fit signals. Technical skills don't overcome fundamental misalignment with startup pace
  • Rushing the process. Early-stage hires are critical; better to wait for right person than rush
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Grade B+AI Skill Framework
Scorecard
Criteria Breakdown
Quick Start
11/15
Workflow
11/15
Examples
15/20
Completeness
15/20
Format
11/15
Conciseness
11/15