Skill Audit & Risk Report for Students

SkillzBench is building the independent testing lab that verifies, benchmarks, and risk-scores every AI agent skill your security operation relies on — so you know exactly what helps and what's a liability.

The Unvetted AI Skills Quietly Creating Risk in Security Operations

Unvetted AI risk

Roughly 1 in 4 AI Skills Carries a Vulnerability — And Security Teams Are Pulling Them in Blind

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Published research found ~of agent skills carry at least one vulnerability. While <a href='https://www.fortinet.com/blog/partners/addressing-three-major-pain-points-of-the-cybersecurity-skills-gap' target='_blank' rel='noopener'>80% of organizations worldwide have suffered a breach attributable to a lack of cybersecurity skills and/or awareness</a>, SOC and incident response teams are importing SKILL.md packages from GitHub with zero security review — no license check, no commit pinning, no provenance. It's the software supply chain problem all over again, except now it's instruction packages running inside agents with access to your cloud security posture and tooling.

A 3.4 Million Worker Talent Gap Is Forcing Security Teams to Lean on AI Agents

3.4M gap

The global cybersecurity workforce gap stands at <a href='https://www.csis.org/analysis/cybersecurity-workforce-gap' target='_blank' rel='noopener'>3.4 million workers despite adding 464,000 jobs</a>, and <a href='https://www.fortinet.com/blog/partners/addressing-three-major-pain-points-of-the-cybersecurity-skills-gap' target='_blank' rel='noopener'>52% of organizations struggle to recruit cybersecurity talent while 60% struggle to retain it</a> — mirroring the 100%–300% turnover plaguing physical security and guard operations. Understaffed SOC analyst teams and incident response functions are turning to AI agents to close coverage gaps, but there is no way to vet the skills those agents run on. You wouldn't hire an unvetted contractor for guard tour duty; why let unaudited SKILL.md packages walk your agents' post?

No Standard Exists for Measuring Whether a Skill Actually Helps — or Quietly Degrades Output

70%

Security operations management has no equivalent of a post coverage report for AI tooling: no way to know whether a skill genuinely improves agent output or silently introduces coverage gaps. <a href='https://issa.org/cyber-security-skills-crisis-causing-rapidly-widening-business-problem/' target='_blank' rel='noopener'>70% of organizations report the cybersecurity skills shortage has impacted them</a> — and now teams are layering unaudited, unbenchmarked AI skills on top of already stretched analysts. Vendor claims fill the vacuum where measurement should be, and leadership is asked to approve spend with no defensible numbers.

How SkillzBench <span class="gradient">Audits</span> the AI Skills Behind Security Work

An independent testing lab and control panel that discovers, verifies, benchmarks, and continuously monitors every agent skill your security team uses.

SkillzBench is building five pillars purpose-built for teams that can't afford unvetted tooling. Discover continuously crawls and dedupes skills from GitHub. Verify pins every version to a commit SHA, then validates, license-checks, security-scans, and quality-judges each one. Benchmark runs every skill in a sandbox with the same model — with and without the skill — and publishes the Lift in percentage points. Equip delivers one MCP server plus per-agent loadouts, so each agent only sees the skills and tools you've explicitly toggled on. Stay current tracks upstream changes, re-scans, re-benchmarks, and notifies you. Every skill gets a 0-100 Trust Score built from security, quality, license, maintenance, and community signals — and money never touches scores.

• Every skill version pinned to a commit SHA, security-scanned, license-checked, and quality-judged before it reaches your agents
• Sandboxed benchmarking with the same model, with and without the skill, publishing the Lift in percentage points — always shown separately from ratings
• One MCP server with per-agent loadouts: your SOC analysts' agents see only the vetted skills and tools you toggle on

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

Close the Vulnerability Blind Spot Before It Closes on You

Close the vulnerability blind spot before it closes on you: with ~of skills carrying at least one vulnerability, SkillzBench's Verify pillar security-scans every skill, pins each version to a commit SHA, and license-checks it — then re-scans automatically when upstream changes are detected. Your SOC stops being the layer that finds out the hard way, and your audit trail answers 'which exact version ran, from which commit' without a single spreadsheet.

Know What Actually Improves Agent Output

Know what actually improves agent output: SkillzBench's Benchmark pillar tests every skill in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points. No vendor claims, no guesswork — just numbers you can defend to leadership when justifying AI tooling spend. And because Lift is always shown separately from Trust Scores, a skill can't hide weak performance behind a good rating.

Precise Control Over What Each Agent Sees

Precise control over what each agent sees: per-agent loadouts mean your incident response agent, your SOC analyst agent, and your documentation agent each get only the skills and tools explicitly toggled for their role — nothing more. Combined with org-level allowlists and audit on the Teams tier, this is the least-privilege model your compliance team already understands, applied to AI agents.

How it works

1. Join the Waitlist — SkillzBench is pre-launch. Sign up for the waitlist to be first in line when the testing lab opens and get launch updates as each pillar goes live.
2. Get Your Skills Verified and Benchmarked — Every skill your team uses gets pinned to a commit SHA, security-scanned, license-checked, quality-judged, and benchmarked in a sandbox — producing a 0-100 Trust Score and a published Lift.
3. Equip Agents with Vetted Loadouts — Connect one MCP server, toggle skills and tools per agent, and let SkillzBench track upstream changes, re-scan, re-benchmark, and notify you when anything shifts.

What you get

• Discover: continuous crawling and deduplication of skills from GitHub, so you're not auditing a stale copy
• Verify: commit-SHA version pinning plus validation, license checks, security scans, and quality judgments on every skill
• Benchmark: same-model sandbox testing with and without each skill, publishing Lift in percentage points
• Equip: one MCP server plus per-agent loadouts with per-skill and per-tool toggles
• Stay Current: upstream change tracking with automatic re-scanning, re-benchmarking, and notifications
• Trust Score: a 0-100 composite of security, quality, license, maintenance, and community signals

Quick wins

• Use the Trust Score (0–100) built from security, quality, license, maintenance and community signals as a single gate in your security review checklist before any skill reaches an agent.
• Deduplicate the skills your team finds on GitHub — SkillzBench's Discover pillar continuously crawls and dedupes, so you're not vetting five copies of the same SKILL.md package.

Why this is different

• Independent testing lab — SkillzBench never sells skills, so scores stay clean
• Every version pinned to a commit SHA for exact reproducibility
• Lift is always published separately from ratings — no blended vanity scores
• Money never touches scores, period
• Benchmarking runs with and without the skill on the same model in a sandbox
• Per-agent loadouts mean agents only see what you chose for them
• Continuous monitoring: upstream changes trigger re-scans and re-benchmarks
• Trust Score blends security, quality, license, maintenance, and community signals

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

Per-agent loadouts mean our SOC analysts' agents only see the vetted skills and tools explicitly toggled for their role. Combined with the Trust Score from security, quality, license, maintenance and community signals, that level of control is what sold our compliance team. — Trevor Osei

Seeing Lift published separately from ratings changed how we evaluate AI tooling. If a skill doesn't move the number, it doesn't ship to our agents. — Priya Raghavan

Per-agent loadouts mean our analysts' agents only see vetted skills and tools. That level of control is what sold our compliance team. — Trevor Osei

Frequently asked questions

How does SkillzBench tell me if an AI agent skill is actually a security risk?

Every skill version is pinned to a commit SHA, then security-scanned, license-checked, validated, and quality-judged. Results feed a 0-100 Trust Score built from security, quality, license, maintenance, and community signals. When upstream code changes, the skill is re-scanned so you're notified if a previously clean skill becomes risky.

Can I prove to leadership whether an AI skill actually improves our agents' output?

Yes — that's the Benchmark pillar. Each skill is tested in a sandbox with the same model, with and without the skill installed, and the Lift is published in percentage points. Lift is always shown separately from ratings, so you get a clean, defensible measure of actual impact rather than a blended score.

Is SkillzBench available now, or is it still being built?

SkillzBench is pre-launch and actively being built. The waitlist is live now, and joining it gets you launch updates as the Discover, Verify, Benchmark, Equip, and Stay Current pillars go public. We don't publish benchmark results or user counts yet because the product isn't public — we'd rather under-promise.

What happens when a skill we depend on gets updated upstream?

The Stay Current pillar tracks upstream changes automatically. When a change is detected, SkillzBench re-scans and re-benchmarks the skill and notifies you, so you can decide whether to keep the pinned version or move to the new one with full knowledge of the security and performance delta.

How is this different from just reading a skill's GitHub stars before adopting it?

Stars and community sentiment are only one input. SkillzBench adds commit-SHA version pinning, security scanning, license checks, quality judgments, and sandboxed same-model benchmarking with published Lift. Plus, per-agent loadouts mean your agents only see the skills you've explicitly toggled on — community popularity never overrides your control.