Skill Audit & Risk Report for Marketers

SkillzBench is building the independent testing lab for AI agent skills: security-scan, license-check, and benchmark every SKILL.md package your security team's agents touch, with a 0-100 trust score and measured Lift.

The Cybersecurity Skills Gap Just Collided With the AI Agent Skills Gap

unvetted agent skills

Understaffed security teams can't afford a new attack surface — and 4.8 million open roles say they won't get one

The global cybersecurity talent gap has reached 4.8 million unfilled positions (2025 ISC2 Workforce Study), and 45 percent of organizations report a problematic cybersecurity skills shortage. Meanwhile, your analysts' AI agents are pulling SKILL.md instruction packages from GitHub with no vetting — and published research found ~of skills carry at least one vulnerability. In a zero-trust architecture, every unvetted skill is an unmonitored data-movement path into your environment, and no one on a depleted SOC team has hours to audit them.

Every breach costs $550,000 more when your team is short-staffed

87%

Organizations with insufficiently staffed security teams paid $550,000 more per breach on average (IBM 2024 Cost of a Data Breach Report), and 87% of organizations experienced at least one breach last year — with more than half suffering losses exceeding $1 million (Fortinet 2024 Skills Gap Report). An unvetted agent skill that quietly redirects outputs or exfiltrates context is exactly the kind of diminished-visibility data movement that makes breaches harder to detect, especially for SOC teams already drowning in alert fatigue.

No visibility into what your agents actually ran

Your SIEM logs your network; nothing logs your agents' skills. When a Claude Code, Codex, or Cursor agent loaded a security playbook SKILL.md last Tuesday, where did it come from, which version was it, and did an upstream commit introduce new risk before this morning's run? Without version pinning and continuous re-scanning, your agent supply chain is a blind spot no threat-hunting playbook covers.

How SkillzBench Turns Unvetted Skills Into <span class="gradient">Skills, Proven</span>

An independent testing lab and control panel for the agent skills your security operation depends on

SkillzBench is building five pillars for agent-skill trust: Discover (continuously crawl and dedupe skills from GitHub), Verify (pin every version to a commit SHA, then validate, license-check, security-scan, and quality-judge it), Benchmark (test every skill in a sandbox with the same model, with and without the skill, and publish the Lift in percentage points), Equip (one MCP server plus per-agent loadouts, so an agent only sees the skills and tools you've toggled on), and Stay current (track upstream changes, re-scan, re-benchmark, and notify). Every skill gets a 0-100 trust score built from security, quality, license, maintenance, and community signals — and Lift is always shown separately from ratings, because money never touches scores.

• Security-scan and license-check every skill version, pinned to a commit SHA, before it reaches your agents
• Sandbox-benchmark each skill with the same model, with and without it, and see the measured Lift in percentage points
• Control exactly what each agent sees with one MCP server and per-agent loadouts — plus re-scans and notifications when upstream skills change

Why Security Teams <span class="gradient">Join the SkillzBench Waitlist</span>

Close the visibility gap before it becomes a breach

Apply zero-trust to your AI tooling itself. SkillzBench's per-agent loadouts mean each agent only sees the vetted skills and tools you explicitly toggled on — nothing else. For a vCISO or MSSP managing multiple client environments, that's an auditable, allowlist-driven posture for agent skills, not another unmanaged attack surface.

Know which skills actually help — with numbers, not vibes

Know which skills actually help — with numbers, not vibes. SkillzBench benchmarks every skill in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points. Before that incident-response SKILL.md touches a production agent, you'll know whether it measurably improves output or just adds risk.

Stay current without adding headcount

Stay current without adding headcount. SkillzBench tracks upstream changes to your pinned skills, re-scans, re-benchmarks, and notifies you — continuous monitoring for your agent supply chain, the kind of vigilance your team can't staff for. Every version is pinned to a commit SHA, security-scanned, and license-checked.

How it works

1. Join the waitlist — SkillzBench is pre-launch and actively being built. Sign up for the newsletter to get early access to the Discover, Verify, and Benchmark pillars as they go live.
2. Get your skills verified and benchmarked — Every skill version is pinned to a commit SHA, security-scanned, license-checked, quality-judged, and sandbox-tested with the same model to produce a 0-100 trust score and a measured Lift.
3. Equip your agents with proven skills only — Connect one MCP server, build per-agent loadouts by toggling skills and tools, and let SkillzBench notify you when upstream changes need a re-scan. Your agents only ever see what you chose.

What you get

• Discover: continuous crawling and deduplication of skills from GitHub so you're not auditing blind
• Verify: validation, license-check, security-scan, and quality-judgment on every pinned skill version
• Benchmark: same-model sandbox testing with and without each skill, publishing Lift in percentage points
• Equip: one MCP server plus per-agent loadouts — toggle skills and tools per agent with full control
• Stay current: upstream change tracking with automatic re-scans, re-benchmarks, and notifications
• Trust score 0-100 combining security, quality, license, maintenance, and community signals

Quick wins

• Add SkillzBench's trust scores (0-100 from security, quality, license, maintenance, and community signals) to your agent onboarding checklist so no unvetted skill reaches a production agent.
• Use per-agent loadouts to enforce skill allowlists per client environment — one MCP server, and each agent only sees what was explicitly chosen.

Why this is different

• Independent testing lab — not a skill marketplace with incentive conflicts
• Every skill version pinned to a commit SHA, never a floating 'latest'
• Lift shown separately from ratings; money never touches scores
• Benchmarks run in a sandbox with the same model, with and without the skill
• One MCP server with per-agent loadouts — agents see only what you choose
• Upstream changes trigger re-scans, re-benchmarks, and notifications automatically
• Trust score 0-100 from security, quality, license, maintenance, and community signals
• Launch scope is instruction-only SKILL.md skills; script-bearing skills link out only

Built by AIQ Labs' independent testing lab — where Lift is always shown separately from ratings and money never touches scores. Waitlist open now ahead of launch.

We're on the waitlist because commit-SHA pinning solves our biggest worry: knowing exactly which version of a skill our agents ran last week when the upstream repo changed. That's the audit trail my clients' compliance reviews keep asking about. — Marcus Delgado

As an MSSP, per-agent loadouts are what sold me. Being able to say an agent only sees the skills and tools we explicitly toggled on is the zero-trust story our clients have been asking about for AI — applied to the tooling itself, not just the network. — Priya Raghunathan

I write security-focused SKILL.md skills, and a benchmark that tests with and without the skill on the same model — with Lift shown separately from ratings — is exactly the credibility layer this ecosystem was missing. — Tobias Wren

Frequently asked questions

How does SkillzBench handle script-bearing skills that might contain malicious code?

Launch scope is instruction-only skills — SKILL.md packages plus text references. Script-bearing skills are listed link-out only, so nothing executable is distributed through the platform. Instruction-only skills are still security-scanned, license-checked, and quality-judged before earning a trust score.

Can SkillzBench help with NIS2 compliance documentation of our AI tooling?

SkillzBench is building Teams-tier features aimed at org loadouts, allowlists, and audit — designed so organizations can document which skills were permitted, which versions (pinned to commit SHAs) were in use, and how they were scanned. Note that SkillzBench is pre-launch; these capabilities are in active development, and the waitlist is live now.

What exactly is the 'Lift' metric, and can I trust it more than vendor marketing claims?

Lift is the measured performance difference from testing a skill in a sandbox with the same model, with and without the skill, expressed in percentage points. SkillzBench always shows Lift separately from trust ratings, and money never touches scores — so a skill author can't pay for a better number.

When can my security team actually start using SkillzBench?

SkillzBench is pre-launch and actively being built — the waitlist/newsletter is live, but the product isn't public yet. Join the waitlist for early access as the Discover, Verify, Benchmark, Equip, and Stay-current pillars come online. Pricing is planned around a generous free tier, Pro at roughly $9-15/mo, and Teams at roughly $15-25/seat/mo.

How is this different from just letting our agents pull skills from GitHub directly?

Raw GitHub gives you no version pinning, no security scan, no license check, and no performance measurement. SkillzBench pins every version to a commit SHA, dedupes across repos, publishes a 0-100 trust score from security, quality, license, maintenance, and community signals, benchmarks Lift in a sandbox, and re-scans and notifies you when upstream changes land — all delivered through one MCP server with per-agent loadouts.