Question 1

What is MCP protocol and how does SkillzBench fit into my MCP implementation strategy?

Accepted Answer

The Model Context Protocol (MCP) is a standard for connecting AI agents to external tools and data. SkillzBench sits on top of your MCP setup: it verifies and benchmarks the agent skills (SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents) you might load, then serves them through one MCP server with per-agent loadouts. SkillzBench is pre-launch — join the waitlist for early access.

Question 2

How does SkillzBench handle MCP security risks like tool poisoning and supply chain vulnerabilities?

Accepted Answer

Every skill version is pinned to a commit SHA, then validated, license-checked, security-scanned, and quality-judged. A trust score from 0-100 combines security, quality, license, maintenance, and community signals so you can gauge risk before loading anything. This directly addresses the security concerns that 64% of software teams cite as their top MCP adoption obstacle.

Question 3

Does SkillzBench support script-bearing skills or only instruction-only SKILL.md packages?

Accepted Answer

At launch, the scope is instruction-only skills — SKILL.md files plus text references. Script-bearing skills will be listed link-out only, so you still get discovery and pointers without SkillzBench executing unvetted code.

Question 4

When can I actually start using SkillzBench, and what will it cost?

Accepted Answer

SkillzBench is pre-launch and actively being built; the waitlist and newsletter are live now. The pricing hypothesis includes a generous free tier, Pro at roughly $9-15/mo for more agents and unlimited enabled skills, Teams at roughly $15-25/seat/mo with org loadouts, allowlists, and audit, and a Creator tier around $5-10/mo for verified authors.

Question 5

How is SkillzBench different from just browsing GitHub or public MCP server directories for skills?

Accepted Answer

Directories list; SkillzBench tests. It dedupes skills crawled from GitHub, pins versions to commit SHAs, security-scans and license-checks them, and benchmarks each one in a sandbox with the same model — with and without the skill — to publish a real Lift in percentage points. Lift is always shown separately from ratings, and money never touches scores, so rankings stay independent.

MCP Server Discovery for Writers