Custom Skill Development for Product Managers

SkillzBench is building the testing lab and control plane for AI agent skills — so every SKILL.md package you develop, deploy, or approve is verified, benchmarked, and measured for real Lift.

The Hidden Risks Derailing Enterprise AI Agent Deployment

Unverified Agent Skills

Vulnerable SKILL.md packages slip straight into production agents

~of skills carry at least one vulnerability

Agent skills — instruction packages for Claude Code, Codex, Cursor and Gemini CLI — are everywhere, but nobody can tell which ones actually help and which are risky. Published research found ~of skills carry at least one vulnerability, and with Gartner predicting more than 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear business value, or insufficient risk controls, copying a skill from GitHub straight into your agent context is an unmanaged risk. Without version pinning, license checks and security scans, every skill your agent loads is an unaudited dependency in your context engineering pipeline.

Tool overload causes agent decision paralysis

23% cite tool-use coordination as top challenge

Organizations commonly expose 20-30 tools to a single agent, causing decision paralysis and unreliable outputs. In a dataset of 3,191 Stack Overflow questions on building AI agents, tool-use coordination was the top operational challenge at 23%. This is agent sprawl in action: every unfiltered MCP server and unvetted skill widens the tool surface your routing agent must reason over, degrading output quality instead of improving it. Without per-agent loadouts, your specialized sub-agents see everything — and reliably use almost none of it well.

No maturity layer for AI agent testing and evaluation

Only 6% are true AI high performers

AI agents entered enterprises without equivalents of unit testing, integration testing, QA pipelines and monitoring — pilots succeed while production fails. Only 6% of companies using AI are true AI high performers, exposing the gap between AI usage and real results. With 62% of organizations at least experimenting with AI agents and 23% actively scaling agentic AI in at least one business function, the missing piece is an evaluation flywheel: sandboxed, repeatable benchmarking that measures whether a skill actually improves agent output before it ships.

How SkillzBench <span class="gradient">Proves</span> Every Agent Skill You Ship

A testing lab and control plane for SKILL.md instruction packages across Claude Code, Codex, Cursor, Gemini CLI and chat-app agents.

SkillzBench is building five pillars that take agent skills from unvetted GitHub repos to governed, benchmarked assets. 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 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 — toggle skills and tools per agent so the agent only sees what was chosen. Stay current tracks upstream changes, re-scans, re-benchmarks, and notifies you when anything shifts.

• Every skill version pinned to a commit SHA, with validation, license checks, security scans, and quality judging before it ever reaches your agents.
• Sandboxed benchmarking with the same model, with and without the skill, publishing Lift in percentage points — always shown separately from ratings, and money never touches scores.
• Per-agent loadouts via one MCP server: toggle skills and tools per agent so each agent only sees what was deliberately chosen — no more 20-tool sprawl.

Why Product Managers <span class="gradient">Choose SkillzBench</span>

Cut risk before skills reach production

Cut risk before skills reach production: SkillzBench is building a Verify pipeline that pins every skill version to a commit SHA, then validates, license-checks, security-scans and quality-judges it before it ever touches your agent. With ~of skills carrying at least one vulnerability, this closes the risk gap Gartner warns will cancel more than 40% of agentic AI projects by 2027.

Measure real Lift, not vibes

Measure real Lift, not vibes: SkillzBench's sandboxed benchmarking runs the same model with and without each skill and publishes the Lift in percentage points — defensible evidence for stakeholders on which skills actually improve agent output. Lift is always shown separately from ratings, and money never touches scores.

End tool overload with per-agent loadouts

End tool overload with per-agent loadouts: instead of exposing 20-30 tools to one agent and causing decision paralysis, SkillzBench's single MCP server plus per-agent loadouts let you toggle exactly which skills and tools each agent sees — the agent only sees what was deliberately chosen for it.

How it works

1. Join the waitlist — SkillzBench is pre-launch and actively being built. Join the waitlist to get early access as Discover, Verify, Benchmark, Equip, and Stay current come online.
2. Verify and benchmark your skills — Every skill gets pinned to a commit SHA, validated, license-checked, security-scanned, quality-judged, and benchmarked in a sandbox — with and without the skill — to publish its Lift.
3. Equip agents with proven loadouts — Deploy through one MCP server with per-agent loadouts, then let Stay current track upstream changes, re-scan, re-benchmark, and notify you automatically.

What you get

• Discover: continuous crawling and deduplication of skills from GitHub so you see the full landscape, not fragments.
• Verify: version pinning to commit SHA plus validation, license checks, security scans, and quality judging on every skill.
• Benchmark: sandboxed testing with the same model, with and without the skill, publishing Lift in percentage points.
• Equip: one MCP server plus per-agent loadouts — toggle skills and tools per agent so the agent only sees what was chosen.
• Stay current: upstream change tracking with automatic re-scans, re-benchmarks, and notifications.
• Trust score 0-100 synthesized from security, quality, license, maintenance and community signals.

Quick wins

• Join the SkillzBench waitlist now to be first in line for the Discover pillar — continuously crawled, deduped skills from GitHub with license checks and security scans on every pinned version.
• Audit your current agent stack today: count how many tools and skills each agent sees. If it's in the 20-30 range research links to decision paralysis, plan your per-agent loadouts before launch.

Why this is different

• Independent testing lab — not a skill marketplace with incentive conflicts
• Every version pinned to a commit SHA for full reproducibility
• Lift in percentage points, always shown separately from ratings
• Money never touches scores — trust is earned, not bought
• Trust score 0-100 built from security, quality, license, maintenance and community signals
• Per-agent loadouts: agents only see deliberately chosen skills and tools
• Stay current pillar re-scans and re-benchmarks on upstream changes
• Benchmarked with the same model, with and without the skill, in a sandbox

Built by AIQ Labs' independent testing lab — no marketplace incentives, money never touches scores. Join the waitlist for early access before launch.

I've been waiting for exactly this — a benchmark that runs the same model with and without the skill and publishes the Lift in percentage points. No marketplace hype, just a trust score built from security, quality, license, maintenance and community signals. — Priya Raghavan

The per-agent loadout concept sold me. Our agents currently drown in tools, and the idea that one MCP server serves every agent while each agent only sees what was deliberately toggled for it is exactly how governance should work. — Marcus Delaney

Version pinning to a commit SHA, with automatic re-scans, re-benchmarks and notifications when upstream changes — that's the piece our governance model has been missing. I joined the waitlist the day I read about it. — Elena Vasquez

Frequently asked questions

How does SkillzBench measure whether an agent skill actually improves performance?

The Benchmark pillar tests every skill in a sandbox using the same model, running tasks with and without the skill, then publishes the Lift in percentage points. Lift is always shown separately from ratings and trust scores, and money never touches scores — so you get an honest, reproducible performance signal rather than a marketing claim.

Does SkillzBench support script-bearing skills or only instruction-only SKILL.md packages?

At launch, the scope is instruction-only skills — SKILL.md files plus text references. Script-bearing skills are listed link-out only, so you still get discovery and visibility without SkillzBench executing unvetted code. This keeps the security surface controlled while the verification pipeline matures.

How does SkillzBench address security and license risk in third-party agent skills?

The Verify pillar pins every skill version to a commit SHA, then validates, license-checks, security-scans, and quality-judges it. This matters because published research found roughly of skills carry at least one vulnerability. A trust score from 0 to 100 synthesizes security, quality, license, maintenance, and community signals into one governance-ready number.

Can we 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. This directly addresses the common pattern of exposing 20-30 tools to a single agent, which causes decision paralysis and unreliable outputs.

What happens when an upstream skill we depend on gets updated or changed?

The Stay current pillar tracks upstream changes, re-scans, re-benchmarks, and notifies you. Because every version is pinned to a commit SHA, you always know exactly which version your agents are running, and you're alerted when the upstream skill shifts rather than silently inheriting changes.