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Hr Candidate Assessment

Community
tuanductran
hr-candidate-assessment

Help recruiters and hiring managers design and run structured candidate assessments beyond interviews — skills tests, work samples, assessment centers, and scoring rubrics. Use when asked to design a candidate assessment, build a skills test for [role], create an assessment center, build a scoring rubric, or choose the right assessment method for this role.

Overview

Publishertuanductran
Repositoryhr-skills
Skill namehr-candidate-assessment
Stars
78
Forks
20
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Hr Candidate Assessment 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.
    https://github.com/tuanductran/hr-skills/tree/main/skills/hr-candidate-assessment
  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/tuanductran/hr-skills.git /tmp/hr-skills
mkdir -p .claude/skills
cp -r /tmp/hr-skills/skills/hr-candidate-assessment .claude/skills/hr-candidate-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hr Candidate Assessment 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 Hr Candidate Assessment 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 Hr Candidate Assessment 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.

Candidate assessment

Design and run structured assessments — skills tests, work samples, simulations, and assessment centers — that predict on-the-job performance more reliably than unstructured interviews alone.

Supported tasks

  • Selecting the right assessment method for a given role and level
  • Designing skills tests and work-sample exercises tied to real job tasks
  • Building assessment centers combining multiple exercises for senior roles
  • Creating scoring rubrics that reduce rater subjectivity
  • Calibrating assessors before a hiring round to improve consistency
  • Balancing assessment rigor against candidate time and drop-off risk
  • Evaluating third-party assessment tools and vendors for fit and validity
  • Designing assessments that reduce adverse impact and bias
  • Combining assessment scores with interview data into a hiring decision
  • Communicating assessment results and feedback to candidates
  • Piloting a new assessment before rolling it out broadly
  • Auditing existing assessments for predictive validity over time

Key prompts

Designing assessments

  1. "Design a work-sample exercise for [role] that reflects a real task they'd do in the first 90 days."
  2. "What assessment method — skills test, case study, simulation, or work sample — best fits [role] at [level]?"
  3. "Build an assessment center agenda for a senior [role] combining a presentation, case exercise, and structured interview."
  4. "Design a scoring rubric for [exercise] with clear behavioral anchors at each rating level."

Calibration and fairness

  1. "Write an assessor calibration guide to reduce scoring inconsistency across interviewers for [assessment]."
  2. "Review this assessment exercise for potential adverse impact or unnecessary barriers unrelated to job performance."
  3. "How do we balance assessment thoroughness against candidate drop-off from a lengthy process?"
  4. "How do we handle a case where two calibrated assessors give meaningfully different scores for the same candidate?"

Evaluating tools and results

  1. "What criteria should we use to evaluate a third-party assessment vendor for [role type]?"
  2. "How do we combine assessment scores with interview feedback into a single hiring recommendation?"
  3. "Design a process for auditing whether our [role] assessment actually predicts on-the-job performance."
  4. "What should we do if our assessment vendor cannot show validity evidence linking scores to job performance?"

Candidate-facing communication

  1. "Draft candidate-facing instructions for [assessment exercise] that set clear expectations without giving away the evaluation criteria."
  2. "Write feedback for a candidate who didn't pass the assessment stage, focused on constructive, specific points."
  3. "How much detail about our assessment methodology should we share with candidates before they take it?"
  4. "Draft a response to a candidate who requests a re-assessment after receiving a low score."

Tips

  • Anchor every assessment exercise to a real job task — assessments disconnected from actual work predict poorly and frustrate candidates.
  • Use structured rubrics with behavioral anchors, not just numeric scales, to materially reduce rater bias and inconsistency.
  • Calibrate assessors together before scoring live candidates, especially for subjective exercises like presentations or case studies.
  • Keep total assessment time proportional to role seniority and candidate market competitiveness — over-testing loses strong candidates in competitive markets.
  • Periodically check whether assessment scores actually correlate with post-hire performance; an assessment that isn't predictive is just candidate friction.

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 Hr Candidate Assessment AI skill do?

Help recruiters and hiring managers design and run structured candidate assessments beyond interviews — skills tests, work samples, assessment centers, and scoring rubrics. Use when asked to design a candidate assessment, build a skills test for [role], create an assessment center, build a scoring rubric, or choose the right assessment method for this role.

Why use Hr Candidate Assessment on TypingMind?

Because you install it once and use it with any model. Hr Candidate Assessment 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 Hr Candidate Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tuanductran/hr-skills/tree/main/skills/hr-candidate-assessment. 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 Hr Candidate Assessment?

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 Hr Candidate Assessment?

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

Is the Hr Candidate Assessment AI skill free?

Yes. It is published on GitHub by tuanductran 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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