Interview Designer

Analyze resumes and design interview strategies using evidence-based methodology. Transforms interview prep from "read resume → ask questions" into "define standard → forensic evidence → future simulation". Combines Geoff Smart's Topgrading, Lou Adler's performance-based hiring, and Daniel Kahneman's bias control. Use when preparing for interviews, creating structured interview guides, or designing questions to validate candidate competencies.

MIT-0 · Free to use, modify, and redistribute. No attribution required.
2 · 1.5k · 4 current installs · 4 all-time installs
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Purpose & Capability
Name/description (design interview guides using Scorecard→Forensic→Simulation) matches the provided files and runtime instructions. Required resources are minimal (templates + prose) and appropriate for the stated functionality.
Instruction Scope
SKILL.md stays within interview-design scope: defines workflow, heuristics, and question-generation steps and points to a local template for output. One notable instruction: it explicitly says "This skill uses <Chain of Thought>" and instructs the agent to maintain an "Objective Evaluator" perspective — this is a behavioral prompt for the agent (role-play). It does not reference reading system files, environment variables, or contacting external endpoints. If your environment forbids exposing internal chain-of-thought or you don't want the model to reveal internal reasoning, review or remove that directive before enabling autonomous invocation.
Install Mechanism
No install spec is present and the skill is instruction-only. README shows suggested npx commands for user installation, but those are user-facing instructions and not part of an automated install spec. No downloads, archives, or third-party package installs are declared in the skill bundle.
Credentials
The skill declares no required environment variables, credentials, or config paths. The runtime instructions do not ask for unrelated secrets. This is proportional for an interview-design skill.
Persistence & Privilege
The skill does not request always:true and is user-invocable only. It does not attempt to modify other skills or system-wide settings. Autonomous invocation remains allowed by default (normal for skills), but there are no extra privileges requested.
Assessment
This skill is instruction-only and internally consistent for designing interview guides. Before installing or enabling autonomous use: 1) Review/confirm you are comfortable with the SKILL.md directive to use "Chain of Thought" (it may encourage revealing internal reasoning)—remove or rephrase if you want only final outputs. 2) Inspect the templates for any placeholder handling of candidate PII and ensure your agent/runtime handles resumes and personal data per your privacy rules. 3) Because the skill's source/homepage are unknown, if you plan to run any suggested npx installs (from README) verify the package names and origins — the skill itself has no automated install, so those commands are optional. 4) If you enable autonomous invocation, consider limiting access to candidate data and auditing generated guides for bias or overly adversarial wording (the skill uses pressure-test language that you may want to soften).

Like a lobster shell, security has layers — review code before you run it.

Current versionv1.0.0
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License

MIT-0
Free to use, modify, and redistribute. No attribution required.

SKILL.md

Interview Designer Skill

Core Mission: Elevate interview planning from "glancing at resume and asking questions" to "evidence-based investigation and projection." Operating Mechanism: Define Scorecard (set standards) → Forensic Scan (evidence gathering) → Future Simulation (performance prediction). Prompt Strategy: This skill uses <Chain of Thought>. When executing, maintain an "Objective Evaluator" perspective, seeking both Red Flags and Green Signals.

1. Dynamic War Room (Expert Panel)

Dynamically summon the most matching best minds into the war room based on candidate's role attributes:

  • Geoff Smart (Who): Responsible for Define & Verify.
    • Principle: Scorecard First. Before looking at any resume, clarify what the standard for an "A Player" is.
  • Lou Adler (Performance-based): Responsible for Predict.
    • Principle: Past performance predicts future performance only if the context is similar. Must design simulations for future scenarios.
  • Daniel Kahneman (Bias Control): Responsible for De-bias.
    • Principle: Beware of "confirmation bias." If concerns are found, also seek counter-evidence; if highlights are found, verify their replicability.
  • Domain Expert: Responsible for Depth.

2. Core Execution Workflow

Step 1: Scorecard Definition - Smart's Priority

Don't look at the resume first! Based on JD or role requirements, define A Player standards for this position:

  • Mission: One sentence - why does this role exist?
  • Outcomes: 3-5 specific, measurable results that must be achieved within 12 months.
  • Competencies: Hard/soft skills required to achieve the above outcomes.

Step 2: Forensic Resume Scan - Smart's Forensic

Use Step 1 standards to scan the resume, looking for Gaps (discrepancies) and High Points (highlights):

  • The "Too Good To Be True" Heuristic: Logical gaps behind perfect data.
  • The "Passenger vs Driver" Heuristic: Individual's true contributions under big company halo.
  • The "First Principles" Heuristic: Principle understanding behind technical jargon.

Step 3: Pressure Test & Future Simulation - Adler's Prediction

Design two types of questions:

  1. Pressure Test Scripts (for past): Design Forensic STAR follow-ups targeting Step 2 concerns (originally "torpedo questions," but more objective).
  2. Future Simulation (for future): Design a specific Performance Problem.
    • Example: "We're entering this new market next year, and the biggest obstacle is X. If you join, how would you analyze this problem in your first week?"

3. Question Design Principles

  1. Cannot Be Memorized: Forces candidates to think on the spot (Simulation) or recall painful memories (Pressure Test).
  2. Forced Trade-offs: Choose between two "correct" options to test values.
  3. Detail Granularity: Must be able to probe down to "what diagram did you draw" or "what exact words did you say."

4. Output Format

Directly call templates/interview_guide_template.md to generate the report. Note: When generating the guide, include both [Red Flags] (concerns) and [Green Signals] (highlight verification) to maintain objectivity in assessment.

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