Question 1

How does SkillzBench verify that a SKILL.md skill is safe to use with client agents?

Accepted Answer

SkillzBench's Verify pillar pins every version to a commit SHA, then runs license checks, security scans and quality judging. Each skill also receives a 0-100 Trust score built from security, quality, license, maintenance and community signals. Note that launch scope covers instruction-only skills (SKILL.md plus text references); script-bearing skills are listed link-out only.

Question 2

Can I use one skill across Claude Code, Codex, Cursor and Gemini CLI agents?

Accepted Answer

SkillzBench is built around the SKILL.md instruction-package format used by Claude Code, Codex, Cursor, Gemini CLI and chat-app agents. Its Equip pillar provides one MCP server plus per-agent loadouts, so you can toggle skills and tools per agent and each agent only sees what was chosen for it.

Question 3

What does the benchmark actually measure — how do I know a skill improves my agent?

Accepted Answer

The Benchmark pillar tests every skill in a sandbox using the same model, running the task with and without the skill, and publishes the Lift in percentage points. Lift is always shown separately from Trust ratings, and money never touches scores, so the numbers stay independent.

Question 4

Is SkillzBench available now, and when can my consultancy start using it?

Accepted Answer

SkillzBench is pre-launch and actively being built. The waitlist and newsletter are live today — join now to be notified when the product goes public. We don't claim live skill counts or published benchmark results yet; the Discover, Verify, Benchmark, Equip and Stay-current pillars are what's being built and will offer at launch.

Question 5

How is SkillzBench different from just pulling skills off GitHub myself?

Accepted Answer

GitHub gives you raw files with no verification — and research found roughly of skills carry at least one vulnerability. SkillzBench adds what GitHub can't: independent security scans, license checks, quality judging, a 0-100 Trust score, same-model Lift benchmarks, version pinning to a commit SHA, and automatic re-scanning and re-benchmarking when upstream changes occur.

Verified Agent Skills for Consultants