Custom Skill Development for Founders

SkillzBench is building the testing lab and control plane for agent skills — so every SKILL.md package you ship is verified, benchmarked, and equipped with measurable Lift.

The Hidden Risks Derailing <span class="gradient">Agent Skills</span> Adoption

Unverified, unbenchmarked skills

~of Agent Skills Carry at Least One Vulnerability — And Teams Install Them Blind

~

SKILL.md instruction packages for Claude Code, Codex, Cursor, and Gemini CLI are proliferating across GitHub faster than anyone can vet them. Published research found roughly of agent skills carry at least one vulnerability, yet there is no standard way to know which ones are safe before they reach your agent's context. Most teams install skills straight from a repo with no version pinning, no license check, and no security scan — meaning a single risky skill can quietly shape what your agent does, what it exposes, and what it can be talked into doing.

Gartner Predicts of Agentic AI Projects Will Be Canceled by End of 2027

Escalating costs, unclear business value, and insufficient risk controls are the three killers Gartner cites — and unvetted skills feed all three. When you can't measure whether a skill actually improves agent performance, every addition is a guess. RAND research shows over 80% of AI projects fail to reach production, nearly double typical IT projects, and agentic projects built on unverified skill stacks with no evaluation layer are the most exposed. Without a control plane for what your agents see and use, agentic initiatives stall before they prove value.

Tool Overload: Exposing 20–30 Tools to One Agent Causes Decision Paralysis

23%

Tool-Use Coordination Policies are the top operational challenge, accounting for 23% of developer issues in a Stack Overflow study of 3,191 questions. The pattern is familiar: every new skill adds instructions, references, and tools to the same agent, until it's juggling 20–30 competing capabilities. The result is degraded routing, slower responses, and unpredictable behavior — the opposite of the efficiency gains that drove 88% of senior executives to plan AI budget increases in the next 12 months.

How SkillzBench Makes Skills <span class="gradient">Proven, Not Presumed</span>

A five-pillar pipeline — Discover, Verify, Benchmark, Equip, Stay Current — built specifically for SKILL.md instruction packages across Claude Code, Codex, Cursor, Gemini CLI and chat-app agents.

SkillzBench is AIQ Labs' independent testing lab and control panel for agent skills. We're building a system that continuously crawls and dedupes skills from GitHub, pins every version to a commit SHA, then validates, license-checks, security-scans and quality-judges each one. Every skill gets tested in a sandbox with the same model, with and without the skill, so you see the Lift in percentage points — always separate from ratings, because money never touches scores. A 0-100 Trust Score combines security, quality, license, maintenance and community signals so you know exactly what you're shipping.

• Verify: every skill 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 the skill, publishing Lift in percentage points
• Equip: one MCP server plus per-agent loadouts — your agent only sees the skills and tools you toggle on

Why Skill Builders <span class="gradient">Choose SkillzBench</span>

Know the risk before you ship

SkillzBench is building a Verify pillar that pins every skill version to a commit SHA, then runs validation, license checks, security scans, and quality judging before anything reaches your agent. Instead of pulling a SKILL.md from GitHub and hoping for the best, you get a trust score from 0–100 built on security, quality, license, maintenance, and community signals — so you know the risk before you ship, not after.

Prove the Lift, not the hype

SkillzBench is building a Benchmark pillar that tests every skill in a sandbox with the same model, with and without the skill, and publishes the Lift in percentage points. You stop relying on README hype and start making adoption decisions on measured performance. Critically, Lift is always shown separately from ratings — money never touches scores — so the number you see reflects the skill, not the marketing budget behind it.

Stay current automatically

SkillzBench is building continuous upstream tracking: when a skill's source changes on GitHub, it gets re-scanned, re-benchmarked, and you get notified. A skill that was safe and effective last month can drift after an upstream edit — and stale, unmonitored dependencies are a leading reason AI projects never reach production. Your skill stack stays current without manual re-audits.

How it works

1. Join the waitlist — SkillzBench is pre-launch and actively being built. Join the waitlist to get first access to the Discover, Verify and Benchmark pipeline.
2. Get your skills verified and benchmarked — Once live, your SKILL.md packages get pinned to commit SHAs, security-scanned, license-checked, quality-judged, and sandbox-tested to measure Lift — with priority runs available via benchmark-as-a-service.
3. Equip agents with proven loadouts — Deploy through one MCP server with per-agent loadouts — toggle exactly which skills and tools each agent sees, with a Trust Score and Lift data behind every choice.

What you get

• Discover: continuously crawl and dedupe skills from GitHub so you find what actually exists
• Verify: pin every version to a commit SHA, then validate, license-check, security-scan and quality-judge
• Benchmark: test every skill in a sandbox with the same model, with and without, and publish the Lift
• Equip: one MCP server plus per-agent loadouts — toggle skills and tools per agent
• Stay current: track upstream changes, re-scan, re-benchmark, and notify you
• Trust Score 0-100 combining security, quality, license, maintenance and community signals

Quick wins

• Join the SkillzBench waitlist to get notified the moment benchmark results and trust scores go live — before you install another unvetted skill.
• Audit your current skill stack against the ~vulnerability rate: list every SKILL.md you've installed from GitHub and check whether any has a pinned version, license, or security scan. Most teams can't say yes to all three.

Why this is different

• Independent testing lab — not a marketplace with pay-to-win rankings
• Lift in percentage points, always shown separately from ratings
• Money never touches scores, period
• Every skill version pinned to a commit SHA
• Same-model sandbox testing, with and without the skill
• Per-agent loadouts — agents only see what you choose
• Trust Score 0-100 from five independent signals
• Upstream tracking with automatic re-scan and re-benchmark

Built by AIQ Labs as an independent testing lab — Lift always separate from ratings, money never touches scores.

I was pulling SKILL.md files straight from GitHub with no idea if they helped or hurt. SkillzBench's benchmark approach — same model, with and without the skill, Lift published in percentage points — is exactly the evaluation layer agent development has been missing. I can finally justify which skills stay in my stack. — Marcus Delgado

My Claude Code and Cursor agents were drowning in instructions — 20-plus tools exposed at once and behavior got unpredictable. SkillzBench's per-agent loadouts through one MCP server changed that: I toggle skills per agent and the agent only sees what I chose. It's the Tool-Use Coordination fix I'd been hand-rolling myself. — Priya Venkataraman

Governance is the word. Every version pinned to a commit SHA, security-scanned, license-checked, with a trust score from 0 to 100 — and the Lift shown separately so ratings can't be bought. With research showing a quarter of skills carry vulnerabilities, this is the control plane agent skills have needed since day one. — Tomas Lindqvist

Frequently asked questions

How is this different from just browsing GitHub or a skills marketplace for agent skills?

Marketplaces show popularity; SkillzBench shows proof. Skills are deduped from continuous GitHub crawling, pinned to commit SHAs, security-scanned and license-checked, then benchmarked with the same model to publish real Lift. Plus, upstream changes are tracked and re-benchmarked automatically, and per-agent loadouts via one MCP server mean your agent only sees what you chose.