Team Skill Governance for Small Businesses

SkillzBench is building the independent testing lab and control panel that lets contract security companies verify, benchmark, and govern every AI agent skill their teams use — before it touches your operation.

The Hidden Risks Piling Onto an Already Strained Guard Force

unvetted AI risk

Your Guard Force Is Churning — And Unvetted AI Tools Multiply the Risk

77%

Security guard turnover hit 77% in 2024, with some contract security firms reporting rates as high as 300% (ASIS International). The industry generates 162,300 job openings every year — almost entirely from quits — while BLS projects 0% employment growth through 2034. Your account managers are already spending their days rehiring, retraining, and backfilling standing posts. Layering unvetted AI agent skills into guard tour reporting or SLA compliance workflows on top of that churn means every new hire inherits tools nobody has verified — and one bad skill can silently corrupt incident reports across an entire client portfolio.

Thin Margins Leave No Room for a Costly AI Mistake

6-15%

Contract security typically earns 20-30% gross margin per guard, narrowing to just 6-15% net after overhead — and the median guard wage of $38,370 buys less in real dollars than in 2003 while rent grew 14.3%. With margins that thin, billable vs non-billable overtime disputes, unbilled admin hours, and manual reporting errors already eat the difference. A single AI agent skill that mishandles client site data or drafts unreliable incident reports can turn a thin-margin post into a straight loss — and in manned guarding, client trust is the contract.

Unfilled Posts and Expanded Duties Demand Automation You Can Actually Trust

Unfilled posts for days or weeks cascade into SLA breaches and lost contracts, while guard forces are absorbing expanded non-traditional duties — health and safety (facilities management (and IT security (Firms are rightly turning to AI agents to close the gap in post coverage documentation and reporting. But published research found roughly of agent skills carry at least one vulnerability — and most security operations teams have no way to tell which ones before they touch client-facing workflows.

How SkillzBench Brings <span class="gradient">Proven, Verified Skills</span> to Security Operations

SkillzBench is building an independent testing lab and control panel for AI agent skills, so your security company adopts AI with evidence instead of guesswork.

SkillzBench is AIQ Labs' independent testing lab and control panel for AI agent skills — the SKILL.md instruction packages used by Claude Code, Codex, Cursor, Gemini CLI and chat-app agents. For a security firm running on thin margins and constant guard turnover, SkillzBench will let you discover skills from GitHub, verify every version against a pinned commit SHA with license checks, security scans, and quality judging, and benchmark each skill in a sandbox — testing the same model with and without the skill to publish the Lift in percentage points. Then, through one MCP server with per-agent loadouts, you decide exactly which skills and tools each agent sees. Upstream changes are tracked, re-scanned, and re-benchmarked so your team stays current without surprises. Trust scores from 0-100 are built from security, quality, license, maintenance and community signals — and money never touches scores.

• Verify: every skill version pinned to a commit SHA, with validation, license checks, security scans, and quality judging before your team uses it
• Benchmark: every skill tested in a sandbox with the same model, with and without the skill, so you see the real Lift in percentage points — never a vendor's claim
• Equip: one MCP server with per-agent loadouts — your agents only see the skills and tools you explicitly toggle on, nothing more

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

Cut the Risk Out of AI Adoption

With roughly of published agent skills carrying at least one vulnerability, deploying unvetted AI into guard tour reporting, incident report drafting, or client billing workflows is a liability your 6-15% net margins can't absorb. SkillzBench is building the evidence-based gate: every skill pinned to a commit SHA, security-scanned, license-checked, and scored 0-100 on trust before anything reaches an agent handling client data.

Prove Value Before You Commit

Lift is always shown separately from ratings and money never touches scores. Before a skill touches your scheduling summaries or post coverage documentation, SkillzBench benchmarks it in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points — so you know whether it genuinely improves output or just looks good on a vendor slide.

Stay Current Without Babysitting Upstream Changes

In an industry where turnover is the top operational challenge and nobody has spare hours to audit tooling, SkillzBench will track upstream changes to every skill you enable, re-scan, re-benchmark, and notify you when something shifts. Launch scope stays deliberately conservative: instruction-only SKILL.md packages — script-bearing skills are listed link-out only.

How it works

1. Join the Waitlist — SkillzBench is pre-launch and actively being built. Join the waitlist to get early access to the Discover, Verify, Benchmark, Equip, and Stay Current pillars as they roll out.
2. Discover and Verify Skills — Once live, browse continuously crawled and deduplicated skills from GitHub, each version pinned to a commit SHA with license checks, security scans, and quality judgments — plus a 0-100 trust score.
3. Benchmark, Equip, and Govern — Review published Lift benchmarks, toggle skills and tools per agent through one MCP server, and let SkillzBench track upstream changes so your team always runs proven, current skills.

What you get

• Discover: continuous crawling and deduplication of skills from GitHub so you find what exists without wading through duplicates
• Verify: every version pinned to a commit SHA, with validation, license checks, security scans, and quality judging
• Benchmark: sandbox testing with the same model, with and without each skill, publishing Lift in percentage points
• Equip: one MCP server plus per-agent loadouts — toggle skills and tools per agent, and the agent only sees what was chosen
• Stay current: upstream change tracking, re-scanning, re-benchmarking, and notifications for every skill you rely on
• Trust score: a 0-100 score built from security, quality, license, maintenance, and community signals — never influenced by payments

Quick wins

• Use SkillzBench's trust scores (0-100 from security, quality, license, maintenance, and community signals) to build a shortlist of vetted skills for incident report drafting before your next client QBR — evidence you can put in front of risk-averse account stakeholders.
• Set up per-agent loadouts so admin agents handling scheduling and billing summaries never see the same skill set as agents touching client site data — a clean separation that maps directly to your SLA and data-handling obligations.

Why this is different

• Independent lab — SkillzBench tests skills, it doesn't sell them
• Every version pinned to a commit SHA for exact reproducibility
• Lift shown separately from ratings; money never touches scores
• Benchmarked with the same model, with and without the skill
• Per-agent loadouts: agents only see what you chose
• Trust scores from security, quality, license, maintenance, community signals
• Upstream changes tracked, re-scanned, re-benchmarked, with notifications
• One MCP server governs skills across Claude Code, Codex, Cursor, Gemini CLI

Built by AIQ Labs as an independent testing lab — trust scores that money can't buy, and benchmarks that show real Lift. Join the waitlist today.

We're evaluating AI agents for incident report drafting and post coverage summaries across multiple client sites, but with published research showing of skills carry vulnerabilities, we wanted proof before anything touched client data. SkillzBench's approach — commit-SHA pinning, security scans, and Lift benchmarks kept separate from ratings — is the governance layer this industry has been missing. — Marcus Delgado

What sold me was the per-agent loadout. Our admin agents handle scheduling summaries and billing prep; our field-reporting agents touch client site data. With SkillzBench, each agent only sees the skills and tools we explicitly toggle on — that's real control over what reaches client-facing workflows, not a compliance checkbox. — Priya Raghunathan

With guard turnover at 77%, whatever AI we deploy has to work for brand-new staff on day one — nobody has time to babysit tools. Knowing SkillzBench will re-scan and re-benchmark skills whenever upstream changes happen, and notify us, removes the fear of silent breakage in our reporting workflows. — Tom Whitfield

Frequently asked questions

How does SkillzBench help a contract security company vet AI agent skills before using them on client accounts?

SkillzBench is building a Verify pillar that pins every skill version to a commit SHA and runs validation, license checks, security scans, and quality judging before it reaches your loadouts. Each skill also gets a 0-100 trust score built from security, quality, license, maintenance, and community signals, so you have an evidence-based gate before anything touches client-facing workflows. This matters in an industry where a mishandled client record can cost you the contract.

Can SkillzBench tell me whether an AI skill actually improves our agents' output on guard tour reporting and scheduling summaries?

Yes — that's the Benchmark pillar. SkillzBench tests every skill in a sandbox using the same model, once with and once without the skill, and publishes the Lift in percentage points. Lift is always shown separately from ratings, so you see the measured difference rather than a reviewer's opinion or a vendor's marketing claim.

What does the vulnerability statistic mean for security firms adopting AI tools?

Published research found roughly of agent skills carry at least one vulnerability. For security companies operating on 6-15% net margins with heavy SLA and compliance obligations, deploying unvetted skills is an unmanaged liability. SkillzBench's security scanning is designed to surface those risks before deployment, and script-bearing skills are listed link-out only at launch — the verified scope is instruction-only SKILL.md packages.

When can my security company actually start using SkillzBench?

SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live now. Join the waitlist to be notified as the Discover, Verify, Benchmark, Equip, and Stay Current pillars come online. Early access will start with instruction-only skills, and Teams plans (~$15-25/seat/mo) will add org loadouts, allowlists, and audit once available.

How is SkillzBench different from just grabbing skills off GitHub or trusting an agent's built-in marketplace?

GitHub gives you raw, unvetted files — no version pinning, no security scan, no measured performance. SkillzBench is an independent testing lab: every version is pinned to a commit SHA, scanned and license-checked, benchmarked with the same model with and without the skill, and scored 0-100. Critically, money never touches scores — Lift is always shown separately from ratings, so a paid placement can't masquerade as a quality signal.