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Candidate Evaluation

Checked
pollinations·vlatest·backend·MIT

This skill analyzes candidate responses to assess their suitability for a role, saving time and improving hiring decisions.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add pollinations/pollinations --skill candidate-evaluation -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

pollinations

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned pollinations/pollinations at b5bae6b280.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
b5bae6b280
Manifest
.claude/skills/candidate-evaluation/SKILL.md
Scanned
1 file, 108 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill analyzes GitHub contributors or candidates for engineering roles, specifically within Pollinations. It gathers data from a candidate's GitHub profile using the GitHub API and Model Context Protocol (MCP) to assess their skills and work style. The analysis results in a formatted report including a fit score, strengths, weaknesses, key repositories, and a hiring recommendation presented with ASCII art and a skills matrix.

When to use it

  • When you need to evaluate a potential contributor or candidate for an engineering role.
  • To research GitHub profiles as part of the hiring process.
  • To update the CONTRIBUTORS.md file with analysis of candidates.
  • When discussing "hiring," "candidate," "MLOps," or "evaluate contributor."

Key capabilities

  • GitHub API Integration: Retrieves repository and pull request information using the GitHub API.
  • Model Context Protocol (MCP) Support: Utilizes MCP to access candidate data.
  • Skill Assessment: Evaluates proficiency in Python, DevOps tools (Docker, CI/CD), GPU/ML deployment technologies, Kubernetes, vLLM, TGI, Quantization and CI/CD pipelines.
  • Work Style Analysis: Assesses PR size preference, response time to reviews, documentation quality, and test coverage habits.
  • Formatted Reporting: Generates a structured report with a fit score (1-10), strengths, weaknesses, key repositories, and hiring recommendation using ASCII art and a skills matrix.

Example prompts

  • "Evaluate @username for MLOps role"
  • "Research GitHub profile for {username}"
  • "Add {username} to CONTRIBUTORS.md"

Tips & gotchas

  • The skill relies on the candidate having an active GitHub presence and public repositories.
  • Results are based solely on publicly available information from GitHub; they do not account for other factors in a hiring decision.
  • To get the most accurate results, ensure the username provided is correct and corresponds to the intended candidate's GitHub profile.