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Interviewing Evaluating Candidates

OrganizationPopular
RefoundAI
interviewing-evaluating-candidates

Help users design and execute a high-signal interview process that prioritizes real-world performance over superficial charisma or pedigree.

Overview

PublisherRefoundAI
Repositorylenny-skills
Skill nameinterviewing-evaluating-candidates
Stars
1.3K
Forks
170
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by RefoundAI on GitHub. Read the source before you install it.

Installation

Install the Interviewing Evaluating Candidates AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git /tmp/lenny-skills
mkdir -p .claude/skills
cp -r /tmp/lenny-skills/skills/interviewing-evaluating-candidates .claude/skills/interviewing-evaluating-candidates
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Interviewing Evaluating Candidates in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Interviewing Evaluating Candidates on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Interviewing Evaluating Candidates is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Interviewing and Evaluating Candidates

Move beyond resumes to assess high-fidelity signals like agency, first-principles thinking, and actual craft.

Help the user with interviewing and evaluating candidates using insights from 27 guests and posts across Lenny's Podcast and Newsletter.

How to Help

  1. Define the Role Core - Identify specific competencies and core jobs the candidate must perform based on your unique organizational needs.
  2. Design Practical Assessments - Move from abstract case studies to almost-real-life assignments or paid work trials that mimic the actual job.
  3. Apply Standardized Evaluation - Implement consistent rubrics and thematic questioning to reduce bias and increase signal quality.
  4. Conduct High-Fidelity Reference Checks - Verify performance with past collaborators to triangulate interview signals and uncover long-term patterns.

Core Principles

Prioritize Enthusiastic Rehires

Brian Halligan: "I think CEOs and everyone dramatically overrates their ability to interview, and overrates their gut feeling, and underrates a really high quality blind reference."

The most effective hiring signal is asking a reference if the candidate was in the top 1 percent of employees and if they would enthusiastically rehire them.

Use the Unsell Email

Kevin Yien: "When you get to offer stage, I send an email and I say all the terrible things that are probably going to reinforce their fears. If you can tell them that upfront and they can read that whole email and still be equally excited to join you, find yourself a A+ hire."

Send an email at the offer stage detailing your company's biggest flaws and challenges to ensure the candidate's commitment is based on reality.

Screen for First-Principles Thinking

Melissa Tan: "I think they looked for two main things. They looked for first principles thinkers, so not necessarily your experience, but how do you approach problems, how do you know the right questions to ask? And then create your own framework around that. Dropbox also hired for people that were just really humble, collaborative and team oriented."

Evaluate how candidates create their own frameworks rather than relying on experience to find those capable of driving cross-functional innovation.

Hire Future Strategy Drivers

Peter Deng: "In 6 months, if I'm telling you what to do, I've hired the wrong person. It helps me and the person operate on a different level where the goal is not, did you hit this OKR? The Meta goal becomes, are we calibrating enough? Are we actually getting into a spot where in 6 months you're the one telling me what needs to be done?"

Focus on finding individuals who will eventually drive the strategy and direct their managers within six months of starting.

Stack Rank References First

Shishir Mehrotra: "I generally value the reference check over interview signals. If I had to stack rank in interviews, what is the best signal? The reference check is the top of the list. Those people, they worked with this person sometimes for years, their knowledge, what you're going to get out of 30 minutes of artificial scenarios it's just like never going to compare what a good reference check will give you."

Reference checks offer a high-fidelity signal of long-term performance that artificial interview environments cannot replicate; treat them as your primary signal.

Questions to Help Users

  • "What are the specific jobs or outcomes this hire needs to achieve in their first six months?"
  • "Are you currently using a standardized rubric to evaluate all candidates for this role?"
  • "What real-world problem from your current roadmap could serve as a work trial project?"
  • "How are you screening for cultural traits like humility, agency, or clock speed?"
  • "What is the most common reason candidates fail in this specific role at your company?"
  • "How are you differentiating between a candidate's personal impact versus the success of their previous company?"

Common Mistakes to Flag

  • Over-indexing on interview charisma - Polished interview skills are often poor predictors of actual job performance and technical depth.
  • Hiring for domain experience over first-principles - Relying on established playbooks prevents candidates from solving unique problems in evolving environments.
  • Treating reference checks as a formality - References provide the only longitudinal data on a candidate's behavior and performance over years.
  • Using abstract or hypothetical case studies - Hypotheticals reveal how someone thinks about imaginary problems rather than how they execute on real ones.

Deep Dive

For all 39 sourced insights from 27 guests, see references/guest-insights.md

Related Skills

  • Hiring Product Talent
  • Org Design
  • Building Growth Team
  • Founding Exec Team

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Interviewing Evaluating Candidates AI skill do?

Help users design and execute a high-signal interview process that prioritizes real-world performance over superficial charisma or pedigree.

Why use Interviewing Evaluating Candidates on TypingMind?

Because you install it once and use it with any model. Interviewing Evaluating Candidates is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Interviewing Evaluating Candidates in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RefoundAI/lenny-skills/tree/main/skills/interviewing-evaluating-candidates. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Interviewing Evaluating Candidates?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Interviewing Evaluating Candidates?

As many as you like. As long as a model supports skills, you can use Interviewing Evaluating Candidates with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Interviewing Evaluating Candidates AI skill free?

Yes. It is published on GitHub by RefoundAI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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