Question 1

What exactly does SkillzBench build for recruiting agents?

Accepted Answer

We develop custom SKILL.md instruction packages — instruction-only skills with text references — tailored to recruiting workflows like sourcing, candidate communication, and screening support. They work with Claude Code, Codex, Cursor, Gemini CLI and chat-app agents. Script-bearing skills are listed link-out only, keeping our scope focused on instruction packages we can fully verify.

Question 2

How do I know a custom skill actually improves my agent's performance?

Accepted Answer

Every skill we build is benchmarked in a sandbox with the same model, tested with and without the skill, and we publish the Lift in percentage points. Lift is always shown separately from ratings, and money never touches scores — so you get an honest, independent read on whether a skill earns its place in your agent's loadout.

Question 3

How does this help with EU AI Act compliance for hiring systems?

Accepted Answer

Recruiting falls under the EU AI Act's provisions on high-risk employment systems, so governance matters. Every SkillzBench skill is pinned to a commit SHA, validated, license-checked, security-scanned, and quality-judged, with a trust score from 0-100 built from security, quality, license, maintenance and community signals. That gives you a documented, auditable basis for the skills your agents run.

Question 4

What does the process look like and how long does it take?

Accepted Answer

SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live now. Once we engage, we scope your recruiting workflows, build the SKILL.md packages, then run them through verification and sandbox benchmarking before delivering them via per-agent loadouts on one MCP server. Joining the waitlist puts you first in line as custom development capacity opens up.

Question 5

Why not just grab free skills from GitHub instead?

Accepted Answer

Published research found ~of skills carry at least one vulnerability, and GitHub gives you no benchmark data, no trust score, and no upstream change tracking. SkillzBench continuously crawls and dedupes skills from GitHub, then adds what raw repos never will: version pinning, security scans, sandbox benchmarking with published Lift, and per-agent loadouts that prevent tool-overload decision paralysis.

Custom Skill Development for Recruiters