Question 1

How does SkillzBench measure whether a skill actually improves my data analyst agent's output?

Accepted Answer

SkillzBench's Benchmark pillar tests every skill in a sandbox using the same model, running tasks with and without the skill, then publishes the Lift in percentage points. Lift is always shown separately from ratings and trust scores, so you see raw performance impact without any influence from monetization.

Question 2

What exactly is a per-agent loadout, and how does it work for a data analyst agent?

Accepted Answer

A loadout is the curated set of skills and tools you assign to one specific agent. You toggle skills and tools per agent, and that agent only sees what was chosen — so your data analyst agent gets a focused, verified set of skills while your other agents keep their own separate loadouts. Everything connects through a single MCP server.

Question 3

How does SkillzBench handle security risks in agent skills?

Accepted Answer

Published research found roughly of skills carry at least one vulnerability, so SkillzBench's Verify pillar security-scans every skill, runs license checks, and quality-judges each one before it can be equipped. Every version is pinned to a commit SHA, and the Stay Current pillar re-scans and re-benchmarks when upstream changes occur.

Question 4

Is SkillzBench available now, and when can I start equipping loadouts?

Accepted Answer

SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live today. Joining the waitlist gets you early access as the Discover, Verify, Benchmark, Equip and Stay Current pillars roll out. Launch scope covers instruction-only skills (SKILL.md plus text references); script-bearing skills will be listed link-out only.

Question 5

How is SkillzBench different from just picking skills directly from GitHub?

Accepted Answer

Raw GitHub gives you no validation, no benchmark evidence, and no way to know a skill's real impact or license status. SkillzBench continuously crawls and dedupes skills, pins versions to commit SHAs, assigns a 0-100 trust score from security, quality, license, maintenance and community signals, benchmarks Lift in a sandbox, and delivers everything through one MCP server with per-agent loadouts — plus ongoing re-scanning when upstream skills change.

Curated Agent Loadouts for Data Analysts