Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Analyzes resumes and builds structured interview guides using a three-phase methodology: define role standards, scan for resume evidence, then design questions to test past behavior and simulate future performance. Draws on Topgrading, performance-based hiring, and cognitive bias control frameworks.
Replaces ad-hoc question lists with a scorecard-first process that separates role standards from resume review, reducing confirmation bias.
When to use it
- Preparing a structured guide before a senior engineering interview
- Identifying resume gaps before meeting a candidate
- Designing questions that can't be answered with rehearsed responses
- Calibrating what 'A Player' looks like for a specific role
- Building a consistent scoring framework across multiple interviewers
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'I'm interviewing a candidate for a Head of Growth role. Here's the JD and their resume.'
- 1Define A Player scorecard from the JD: mission, 12-month outcomes, required competencies
- 2Scan resume forensically against scorecard, flagging gaps and highlights using Passenger vs Driver and Too Good To Be True heuristics
- 3Generate Forensic STAR follow-up questions targeting each identified concern
- 4Design a future simulation scenario tied to a real business problem in the JD
- 5Compile full interview guide with Red Flags and Green Signals sections
A structured interview guide with scorecard criteria, targeted pressure-test questions, a future simulation prompt, and objective evaluation notes