Team Skill Governance for Sales Teams

SkillzBench gives security-minded sales teams a control panel to discover, verify, benchmark, and equip AI agent skills — so every skill your team ships is proven, not just installed.

The Cybersecurity Skills Gap Meets the AI Agent Free-for-All

Unvetted AI Skills

Roughly of published agent skills carry at least one vulnerability

~of skills carry at least one vulnerability

Security teams wouldn't run an unvetted binary in production, yet AI agent skills from GitHub are being installed with no security scanning, no license checks, and no validation of what the SKILL.md instructions actually tell the agent to do. Published research found ~of skills carry at least one vulnerability — and in a security-conscious organization, one careless install can turn a productivity tool into a data-exfiltration vector that bypasses your SIEM entirely.

The cybersecurity talent shortage leaves no one to vet agent skills

of organizations face a problematic cybersecurity skills shortage

45 percent of organizations report a problematic cybersecurity skills shortage, and ISC2 counted 4 million unfilled cybersecurity jobs in 2023 — a 13% rise from 2022. Your CISO and security analysts are already stretched across phishing simulations, firewall policy changes, and threat simulation. They do not have bandwidth to manually review every SKILL.md package a team member wants to equip. Unvetted skills become shadow risk — the AI equivalent of an unmanaged endpoint.

Turnover and breach costs compound the risk

$145,000 average replacement cost per security professional

Replacing a cybersecurity professional averages $145,000 once you factor recruitment, training, and lost productivity — while cybercrime costs businesses an estimated $10.5 trillion annually. With 91 percent of organizations feeling susceptible to a major cyberattack, a single unvetted skill that quietly exfiltrates data can undo months of cyber readiness work and turn a sales productivity tool into a breach vector.

How SkillzBench Brings <span class="gradient">Skill Governance</span> to Security-Focused Sales Teams

An independent testing lab and control panel for AI agent skills — so your sales org only equips skills that are verified, benchmarked, and safe.

SkillzBench is AIQ Labs' independent testing lab and control panel for AI agent skills — the SKILL.md instruction packages that power Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents. For security-conscious sales organizations, that means every skill is discovered from GitHub, deduplicated, pinned to a commit SHA, validated, license-checked, security-scanned, and quality-judged before it ever reaches an agent. Then it's benchmarked in a sandbox with the same model, with and without the skill, so you see the Lift in percentage points before you equip anything. One MCP server plus per-agent loadouts let admins toggle exactly which skills and tools each agent sees — nothing more. And when upstream changes, SkillzBench re-scans, re-benchmarks, and notifies you. SkillzBench is currently in active development — join the waitlist to be first in line.

• Verify: every skill version pinned to a commit SHA, with validation, license checks, security scanning, and quality judging — critical for teams battling the cybersecurity skills gap
• Benchmark: every skill tested in a sandbox with the same model, with and without the skill, with the Lift published in percentage points — separate from ratings, always
• Equip: one MCP server plus per-agent loadouts — your sales agents only see the skills and tools an admin explicitly chose for them, a least-privilege approach to AI tooling

Why Security Leaders <span class="gradient">Choose SkillzBench</span>

Security-scanned before your team ever equips a skill

Every skill is security-scanned, license-checked, and quality-judged before your team ever equips it, with a 0–100 trust score built from security, quality, license, maintenance, and community signals. Lean teams stretched thin by the cybersecurity talent shortage get a vetting layer that works like a least-privilege gate for AI — no manual SKILL.md audits required.

Proof of Lift, not vendor promises

SkillzBench benchmarks every skill in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points — always shown separately from ratings, because money never touches scores. You know a skill actually improves agent performance before rolling it out org-wide, not from vendor promises.

Stay current without re-auditing everything yourself

SkillzBench tracks upstream changes to every skill you rely on, then re-scans, re-benchmarks, and notifies you. When a maintainer pushes a change, your team knows before it becomes a cloud security risk or a compliance headache — no more silently mutated dependencies in your agents' instruction packages.

How it works

1. Join the waitlist — SkillzBench is pre-launch and being built now. Join the waitlist to get early access as the Discover, Verify, Benchmark, and Equip pillars come online.
2. Review verified, benchmarked skills — Browse deduplicated skills crawled from GitHub — each pinned to a commit SHA, security-scanned, license-checked, and benchmarked with a published Lift and a 0–100 trust score.
3. Equip your agents with governed loadouts — Connect one MCP server, build per-agent loadouts, and toggle exactly which skills and tools each sales agent sees. Teams tiers add org loadouts, allowlists, and audit.

What you get

• Discover: continuous crawling and deduplication of skills from GitHub, so your team sees one clean catalog instead of scattered forks
• Verify: commit SHA pinning, validation, license checks, security scanning, and quality judging on every skill version
• Benchmark: sandbox testing with the same model, with and without the skill, publishing the Lift in percentage points
• Equip: one MCP server plus per-agent loadouts — agents only see the skills and tools chosen for them
• Stay current: upstream change tracking with automatic re-scanning, re-benchmarking, and notifications
• Trust score 0–100 combining security, quality, license, maintenance, and community signals

Quick wins

• Apply least-privilege access to AI agents: use per-agent loadouts so each agent only sees the skills and tools you explicitly toggled — nothing more.
• Treat skill vetting like vulnerability management: rely on automated security scans, license checks, and trust scores instead of manual SKILL.md reviews your security team doesn't have time for.

Why this is different

• Independent lab — SkillzBench tests skills, it doesn't sell them
• Every skill version pinned to an immutable commit SHA
• Lift shown in percentage points, always separate from ratings
• Money never touches scores — no paid placements, ever
• Per-agent loadouts enforce least-privilege access to skills and tools
• Trust score 0–100 built from security, quality, license, maintenance, community signals
• Upstream changes trigger automatic re-scan, re-benchmark, and notification
• Same model, same sandbox — with and without the skill, every time

SkillzBench is built by AIQ Labs' independent testing lab — money never touches scores, and Lift is always shown separately from ratings.

We stopped guessing which AI skills our analysts and sales reps should equip. Seeing the Lift in percentage points — benchmarked in a sandbox with the same model, with and without the skill — means we only roll out what's proven, the same way we validate any other tool before it touches our environment. — Marcus Feldman

The per-agent loadouts sold us immediately. Each agent only sees the skills and tools we explicitly toggled through the MCP server — that's least-privilege access applied to AI agents, exactly how we run the rest of our security posture. — Priya Raghavan

Every skill version is pinned to a commit SHA, security-scanned, and license-checked before it touches an agent. With roughly a quarter of published skills carrying vulnerabilities, that level of vetting isn't optional for us — it's table stakes. — Deshawn Whitaker

Frequently asked questions

How does SkillzBench handle the risk that published AI agent skills contain vulnerabilities?

Published research found roughly of skills carry at least one vulnerability. SkillzBench's Verify pillar security-scans every skill, validates it, checks its license, and pins each version to a commit SHA. Script-bearing skills are listed link-out only at launch, and every skill carries a 0–100 trust score built from security, quality, license, maintenance, and community signals — so your team can see risk before equipping anything.

Can we control exactly which skills each sales agent can use?

Yes. SkillzBench's Equip pillar provides one MCP server plus per-agent loadouts: admins toggle skills and tools per agent, and the agent only sees what was chosen. Teams-tier plans (hypothesized at ~$15–25/seat/mo) add org loadouts, allowlists, and audit for organization-wide governance.

What happens when a skill's maintainer pushes an update upstream?

SkillzBench's Stay Current pillar tracks upstream changes, re-scans, re-benchmarks, and notifies you. That means a maintenance change to a skill your sales team relies on never slips in unannounced — you'll know it changed and whether its Lift and trust score moved.

How is SkillzBench different from just browsing GitHub for skills ourselves?

Browsing GitHub means manual vetting, duplicate forks, no version pinning, and no performance evidence. SkillzBench deduplicates skills from continuous crawling, pins every version to a commit SHA, security-scans and license-checks each one, benchmarks Lift with the same model in a sandbox, and lets you equip agents through governed per-agent loadouts via one MCP server — plus notifications when upstream changes. Note that SkillzBench is pre-launch; the waitlist is live and the product is actively being built.