Curated Agent Loadouts for Startup Teams

SkillzBench is building the testing lab and control panel that discovers, verifies, and benchmarks AI agent skills — so your team equips agents only with skills that measurably lift performance.

Why Most Startup Teams Equip Agents Blind

Unverified skills, invisible risk

88% of AI agent projects never reach production — and unvetted skills are a leading culprit

88%

Demos succeed, production fails. 88% of AI agent projects stall before production, and Gartner predicts more than 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear business value, or insufficient risk controls. When teams bolt on SKILL.md instruction packages from GitHub without evidence they improve performance, they're gambling with a $340,000 average failure cost. SkillzBench is building the benchmarking layer that's missing: every skill tested in a sandbox with the same model, with and without the skill, so you see the Lift in percentage points before you ever equip it.

Roughly of published agent skills carry at least one vulnerability — and nobody can tell which ones

Published research found ~of skills carry at least one vulnerability, yet teams routinely copy SKILL.md files straight from GitHub with no security scan, no license check, and no version pinning. Security review gaps — missing documentation, access control frameworks, and audit log infrastructure — kill more agent projects than the vulnerabilities themselves. SkillzBench is building per-version verification: every skill version pinned to a commit SHA, validated, license-checked, security-scanned, and quality-judged, with a 0-100 trust score built from security, quality, license, maintenance and community signals.

Tool and skill overload causes decision paralysis — agents fail 70-95% of the time in production

70-95%

Exposing 20-30 tools to a single agent causes decision paralysis, and agents fail between 70% and 95% of the time in production depending on task complexity. Yet 79% of executives say AI agents are already delivering measurable value — the gap is in how agents are equipped. Context engineering means curating exactly what each agent sees. SkillzBench's per-agent loadouts are being built so you toggle skills and tools per agent, and each agent only sees what was chosen — no more dumping every skill and tool into a single context window.

How SkillzBench <span class="gradient">Equips Your Agents</span> With Proven Skills

A control panel for AI agent skills: discover, verify, benchmark, then equip each agent with a curated loadout of skills and tools.

SkillzBench is AIQ Labs' independent testing lab and control panel for AI agent skills — the SKILL.md instruction packages powering Claude Code, Codex, Cursor, Gemini CLI and chat-app agents. We're building a pipeline that continuously crawls and dedupes skills from GitHub, pins every version to a commit SHA, validates, license-checks, security-scans, and quality-judges each one, then benchmarks every skill in a sandbox with the same model — with and without the skill — and publishes the Lift in percentage points. Through one MCP server plus per-agent loadouts, your team toggles skills and tools per agent, and each agent only sees what was chosen for it. No more equipping agents blind.

• Per-agent loadouts: toggle skills and tools per agent so each agent only sees what was chosen — no tool overload
• Every skill version pinned to a commit SHA, with validation, license checks, security scans, and quality judging
• Benchmarked Lift in percentage points, shown separately from a 0-100 trust score built from security, quality, license, maintenance and community signals

Why Startup Teams <span class="gradient">Choose SkillzBench</span>

Know the Lift before you equip

Know the Lift before you equip. SkillzBench is building sandbox benchmarks that test every skill with the same model, with and without the skill, and publish the Lift in percentage points — always shown separately from ratings, and money never touches scores. That means no more guessing whether a SKILL.md package actually helps your Claude Code, Codex, Cursor, or Gemini CLI agent. It's the defensible evidence layer that addresses the core driver of failure: 88% of AI agent projects never reach production.

Security and license clarity, per version

Security and license clarity, per version. Every skill version SkillzBench verifies is pinned to a commit SHA, validated, license-checked, security-scanned, and quality-judged — with a 0-100 trust score from security, quality, license, maintenance and community signals. Launch scope covers instruction-only skills (SKILL.md plus text references); script-bearing skills are listed link-out only, so you always know exactly what you're equipping.

Stay current without babysitting

Stay current without babysitting. SkillzBench will track upstream changes to the skills in your loadouts, re-scan, re-benchmark, and notify you when something shifts — so your per-agent loadouts stay safe and effective as skills evolve. Combined with the single MCP server that serves your chosen skills to Claude Code, Codex, Cursor, Gemini CLI and chat-app agents, your AI agent governance runs without manual overhead.

How it works

1. Join the waitlist — SkillzBench is pre-launch. Join the waitlist to get early access to the testing lab and control panel for AI agent skills.
2. Discover and verify — Browse continuously crawled, deduped skills from GitHub — each pinned to a commit SHA, validated, license-checked, security-scanned, and quality-judged with a 0-100 trust score.
3. Equip curated loadouts — Connect one MCP server, toggle skills and tools per agent, and deploy loadouts where each agent only sees what was chosen — with published Lift showing exactly what each skill contributes.

What you get

• Discover: continuous crawling and deduplication of skills from GitHub so your team starts from a clean, current catalog
• Verify: validation, license checks, security scans, and quality judging on every pinned skill version
• Benchmark: sandbox testing with the same model, with and without the skill, publishing Lift in percentage points
• Equip: one MCP server plus per-agent loadouts — toggle skills and tools so each agent only sees what was chosen
• Stay current: upstream change tracking with re-scanning, re-benchmarking, and notifications
• Trust score: a 0-100 composite from security, quality, license, maintenance and community signals — with money never touching scores

Quick wins

• Audit your current agents' context windows: if any single agent sees 20-30 tools or skills, start planning per-agent loadouts — tool overload causes decision paralysis and production failure rates of 70-95%.
• Before equipping any skill from GitHub, check whether it's pinned to a commit SHA and license-checked — roughly of published skills carry at least one vulnerability.

Why this is different

• Independent testing lab — money never touches scores, ever
• Lift always shown separately from trust ratings
• Every skill version pinned to a commit SHA
• Benchmarks run with and without the skill, same model
• Per-agent loadouts via one MCP server
• Continuous re-scan and re-benchmark on upstream changes
• Trust score from security, quality, license, maintenance and community signals
• Launch scope is instruction-only skills — no hidden scripts

SkillzBench is AIQ Labs' independent testing lab — built on published research showing ~of skills carry at least one vulnerability. Money never touches scores.

We used to copy SKILL.md files from GitHub and hope for the best. SkillzBench's per-agent loadouts mean each agent only sees the skills and tools we've toggled for it — and the trust score built from security, quality, license, maintenance and community signals tells us why. — Marcus Feldman

The with-and-without benchmark design is exactly what we needed. Seeing Lift in percentage points — always separate from ratings, with money never touching scores — finally gave our team a defensible way to pick skills instead of arguing over GitHub stars. — Priya Raghunathan

Version pinning to a commit SHA plus continuous re-scanning and re-benchmarking on upstream changes is the governance layer we couldn't build ourselves as a small startup. One MCP server serving curated per-agent loadouts across our whole toolchain replaced a pile of manual review spreadsheets. — Devon Okafor

Frequently asked questions

How is this different from just browsing skills on GitHub or a community marketplace?

GitHub shows you code, not evidence. SkillzBench adds validation, license checks, security scans, quality judging, and a 0-100 trust score from security, quality, license, maintenance and community signals — plus sandbox benchmarks with published Lift. It's an independent lab: money never touches scores.