Custom Skill Development for Data Analysts

SkillzBench is building the testing lab and control panel for AI agent skills — so the skills your data analysts rely on are verified, benchmarked, and proven to lift performance.

The Hidden Costs of Unverified Agent Skills

Unverified Skills

Roughly of agent skills ship with at least one vulnerability — and most teams can't tell which ones

of agent skills carry at least one vulnerability

Published research found approximately of agent skills carry at least one vulnerability. As teams adopt SKILL.md instruction packages across Claude Code, Codex, Cursor and Gemini CLI, they're bolting unvetted third-party instructions directly onto agents that touch production code and sensitive data. Without security scanning, license checks and version pinning, every new skill added to a loadout is a blind trust decision — and with 46% of UK businesses struggling to recruit for data roles, teams leaning on AI agents to close the skills gap are compounding silent risk instead of managing it.

No evidence a skill actually improves output — the agent-tooling version of the 4% measurement gap

Only 4% of L&D leaders measure skill-building outcomes in business terms

Only 4% of L&D leaders report on skill-building outcomes in terms of business results — and agent skills have the same accountability problem. The data analytics market was valued at USD 85.5 billion in 2025 and is projected to reach USD 738.6 billion by 2033, yet teams deploying skills for analytics work have no way to prove a given SKILL.md package actually improves agent output. Tooling budgets get spent on skills chosen by GitHub star count and vibes, not measured Lift — and competency mapping for your agents' capabilities stays guesswork.

The data skills shortage is widening — and unvetted skills fill the gap with hidden risk

46% of UK businesses struggle to recruit for data roles

The UK alone has a shortfall of 178,000 to 234,000 individuals with data competency, and 66% of surveyed companies report that filling skills gaps and finding new talent is a major challenge. That pressure pushes organizations toward AI agents and upskilling programs as the fastest lever — but when 29% of L&D professionals globally cite addressing the skills gap as their top concern, bolting unvetted community skills onto those agents swaps one skills problem for a security and quality problem nobody is measuring.

How SkillzBench Puts <span class="gradient">Proof Behind Every Skill</span>

A testing lab and control panel for the agent skills your data analysts depend on — discover, verify, benchmark, equip, and stay current.

SkillzBench, AIQ Labs' independent testing lab for agent skills, is building five pillars to take the guesswork out of skill adoption. Discover continuously crawls and dedupes skills from GitHub. Verify pins every version to a commit SHA and runs validation, license checks, security scans and quality judging. Benchmark tests every skill in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points. Equip gives you one MCP server plus per-agent loadouts, so each agent only sees the skills and tools you chose for it. And Stay Current tracks upstream changes, re-scans, re-benchmarks and notifies you — so a custom skill built for your data analysts stays safe and effective long after you deploy it.

• Benchmark every skill in a sandbox with the same model, with and without the skill, and see the Lift in percentage points — evidence, not vibes.
• Pin every skill version to a commit SHA with validation, license checks, security scans and quality judging, rolled into a 0-100 trust score.
• Control exactly what each agent sees with one MCP server and per-agent loadouts — toggle skills and tools per agent.

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

Deploy skills without deploying risk

Deploy skills without deploying risk: SkillzBench is building a Verify pipeline that pins every skill version to a commit SHA, validates the package, license-checks it, security-scans it and quality-judges it before it ever reaches your analysts' agents. Combined with per-agent loadouts — where each agent only sees the skills and tools you've explicitly toggled on — you get least-privilege by default for teams handling sensitive data.

Measure what a skill is actually worth

Measure what a skill is actually worth: SkillzBench's Benchmark pillar will test every skill in a sandbox with the same model, with and without the skill, and publish the Lift in percentage points. Lift is always shown separately from the 0-100 Trust score, and money never touches scores — so the numbers you use to justify agent tooling budgets to stakeholders stay independent and credible.

Keep pace with a fast-moving ecosystem

Keep pace with a fast-moving ecosystem: the Stay Current pillar will track upstream changes to every skill in your loadouts, re-scan and re-benchmark on change, and notify you — so a maintainer's weekend update can't silently introduce a vulnerability or degrade performance between your reviews.

How it works

1. Join the waitlist — SkillzBench is pre-launch and actively being built. Join the waitlist to get early access as the Discover, Verify and Benchmark pillars come online.
2. Verify and benchmark your skills — Once live, every skill version is pinned to a commit SHA, security-scanned, license-checked and benchmarked in a sandbox — with and without the skill — to publish its Lift.
3. Equip your agents with confidence — Connect one MCP server, build per-agent loadouts, and toggle exactly which skills and tools each data analyst's agent can see — then get notified as upstream changes are re-scanned and re-benchmarked.

What you get

• Discover: Continuously crawl and dedupe skills from GitHub so your team finds the right data-analysis skills without wading through duplicates.
• Verify: Pin every version to a commit SHA, then validate, license-check, security-scan and quality-judge each skill before it goes anywhere near your agents.
• Benchmark: Test every skill in a sandbox with the same model, with and without the skill, and publish the Lift in percentage points so you see real impact.
• Equip: One MCP server plus per-agent loadouts — toggle skills and tools per agent, and the agent only sees what was chosen.
• Stay Current: Track upstream changes to every skill, re-scan, re-benchmark and notify your team when something shifts.
• Trust Score: A 0-100 score built from security, quality, license, maintenance and community signals — with Lift always shown separately and money never touching scores.

Quick wins

• Use the Trust score (0-100, built from security, quality, license, maintenance and community signals) as a first-pass filter before any skill enters a team loadout.
• Set up per-agent loadouts so your data-handling agents only see license-checked, security-scanned skills — no blanket installs across the whole team.

Why this is different

• Independent testing lab — SkillzBench never sells the skills it judges.
• Every skill version pinned to a commit SHA for full traceability.
• Lift shown in percentage points, always separate from ratings.
• Money never touches scores — trust scores stay independent.
• One MCP server with per-agent loadouts for precise control.
• Sandbox benchmarks with the same model, with and without the skill.
• Continuous upstream tracking with re-scans, re-benchmarks and notifications.
• Trust score 0-100 built from security, quality, license, maintenance and community signals.

Built by AIQ Labs as an independent testing lab — trust scores and Lift are published separately, and money never touches scores.

The per-agent loadout model is exactly what our team needs — each analyst's agent only sees the skills we've vetted for their workflow, nothing more. When you're handling sensitive data, least-privilege for agent toolchains isn't optional. — Priya Raghavan

Benchmarks that run the same model with and without the skill, then publish the Lift in percentage points — that's the kind of evidence I've never had for agent tooling decisions. And knowing Lift is shown separately from ratings, with money never touching scores, means I can actually defend the numbers in budget reviews. — Marcus Delgado

Pinning every skill version to a commit SHA changes everything for our security review. We finally know exactly what instructions our agents are running — and when Stay Current flags an upstream change, we re-review on our schedule, not after something breaks. — Ingrid Solheim

Frequently asked questions

How does SkillzBench measure whether an agent skill actually improves a data analyst's output?

SkillzBench's Benchmark pillar tests every skill in a sandbox using the same model, running tasks both with and without the skill, and publishes the resulting Lift in percentage points. Because Lift is always shown separately from ratings and money never touches scores, you get an independent, apples-to-apples measure of real impact.

What security checks do skills go through before my team's agents can use them?

The Verify pillar pins every skill version to a commit SHA, then validates the skill, checks its license, runs security scans and applies quality judging. These signals feed into a 0-100 trust score built from security, quality, license, maintenance and community data. Note that at launch, scope covers instruction-only skills (SKILL.md plus text references); script-bearing skills are listed link-out only.

Can I control which skills and tools each individual agent can access?

Yes — the Equip pillar provides one MCP server plus per-agent loadouts. You toggle skills and tools per agent, and the agent only sees what was chosen for it, so a data analyst's agent never gets access to skills outside its approved loadout.

When can I start using SkillzBench, and what does rollout look like?

SkillzBench is pre-launch and actively being built; the waitlist and newsletter are live today. Joining the waitlist gets you early access as the Discover, Verify, Benchmark, Equip and Stay Current pillars come online. Teams tiers with org loadouts, allowlists and audit capabilities are planned alongside a generous free tier.

How is SkillzBench different from just installing skills directly from GitHub?

Skills pulled straight from GitHub come with no version pinning, no security scanning, no license checks and no evidence they help. SkillzBench dedupes crawled skills, pins every version to a commit SHA, security-scans and quality-judges each one, benchmarks Lift in a sandbox, and keeps watching upstream for changes — re-scanning, re-benchmarking and notifying you when a skill you rely on shifts.