Custom Skill Development for Marketers

SkillzBench is building the testing lab and control plane for AI agent skills — discover, verify, benchmark, and equip proven SKILL.md packages for Claude Code, Codex, Cursor, Gemini CLI and chat-app agents.

The Hidden Risks Lurking in Your Agent's Skill Library

Unverified Skills

Roughly of published agent skills carry at least one vulnerability — and most teams install them blind

SKILL.md instruction packages are spreading fast across Claude Code, Codex, Cursor, Gemini CLI and chat-app agents, but published research found that about of agent skills carry at least one vulnerability. Teams keep pulling skills straight from GitHub with no security scan, no license check, no version pinning, and no quality validation — hoping the instruction package they just dropped into an agent's context doesn't break the agent or expose the organization. With 88% of organizations already embedding AI agents into workflows, unvetted skills have become a quiet, compounding risk in the agentic AI stack.

Tool-Use Coordination Policies are the #1 operational headache — driving 23% of developer questions

23%

In a Stack Overflow study, tool-use coordination accounted for 23% of developer questions — the single largest operational challenge in agentic AI. When every agent sees every skill and every tool, context gets polluted, agents call tools they shouldn't, and reasoning traces become impossible to audit. There's no clean control plane for deciding which tools and skills each individual agent should even be able to see, so teams resort to manual configuration that drifts the moment upstream skills change.

Impressive demo, unproven production Lift — agent skill ROI is impossible to justify

88%

Organizations implementing AI agents report 20-35% reductions in operational costs and 30-40% improvements in process efficiency — but only when the skills behind those agents actually work. Today, nobody can prove a SKILL.md package improves output versus running the same agent without it. The leap from impressive demo to reliable production is significant and under-explored, making agentic AI ROI a matter of faith rather than evidence.

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

An independent testing lab and control panel for agent skills — so your marketing agents run only verified, benchmarked, current skills.

SkillzBench is building five pillars for agent skills: Discover (continuously crawl and dedupe skills from GitHub), 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 the skill, and publish the Lift in percentage points), Equip (one MCP server plus per-agent loadouts where the agent only sees what you chose), and Stay current (track upstream changes, re-scan, re-benchmark, and notify). A 0-100 trust score built from security, quality, license, maintenance, and community signals tells you which skills are safe — and Lift is always shown separately from ratings, because money never touches scores.

• Every skill version is pinned to a commit SHA and put through validation, license checks, security scans, and quality judging before it earns a trust score.
• Benchmarking runs each skill in a sandbox with the same model, with and without the skill, publishing the measurable Lift in percentage points.
• Per-agent loadouts via one MCP server mean you toggle skills and tools per agent — the agent only sees exactly what was chosen for it.

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

Deploy skills without the vulnerability roulette

Deploy skills without the vulnerability roulette. SkillzBench is building a Verify pipeline that pins every skill version to a commit SHA, validates it, runs a license check, security-scans it, and quality-judges it — then rolls it all into a 0-100 trust score built from security, quality, license, maintenance and community signals. That gives you a defensible, documented answer to the question every security reviewer asks: 'is this skill safe to run on our agents?'

Prove AI agent ROI with real Lift numbers

Prove agent skill ROI with real Lift numbers. 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. Lift is always shown separately from ratings, and money never touches scores — so the evidence you take to the board is honest, reproducible, and directly tied to the 30-40% process-efficiency gains agentic AI can deliver when skills actually perform.

Clean tool-use coordination, per agent

Clean tool-use coordination, per agent. SkillzBench's Equip pillar gives you one MCP server plus per-agent loadouts: toggle the skills and tools each agent should have, and the agent only sees what was chosen. No more context pollution, no more agents reaching for tools they shouldn't touch — and when upstream skills change, SkillzBench re-scans, re-benchmarks, and notifies you so your loadouts stay current automatically.

How it works

1. Join the waitlist — SkillzBench is pre-launch. Join the waitlist to get early access as the Discover, Verify, Benchmark, Equip, and Stay-current pillars go live.
2. Discover and verify skills — Browse continuously crawled, deduped skills from GitHub — each version pinned to a commit SHA, validated, license-checked, security-scanned, and quality-judged with a 0-100 trust score.
3. Equip your agents with proven skills — Enable benchmarked skills through one MCP server with per-agent loadouts, then let SkillzBench track upstream changes, re-scan, re-benchmark, and notify you when anything shifts.

What you get

• Discover: continuously crawl and dedupe agent skills from GitHub so you're browsing the whole ecosystem, not a hand-picked shelf.
• Verify: pin every version to a commit SHA, then validate, license-check, security-scan, and quality-judge before it earns a trust score.
• Benchmark: test every skill in a sandbox with the same model, with and without the skill, and publish the 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: track upstream changes, re-scan, re-benchmark, and notify you when a skill you rely on changes.
• Trust score 0-100 built from security, quality, license, maintenance, and community signals — with money never touching scores.

Quick wins

• Join the SkillzBench waitlist to get notified the moment the Discover pillar goes live — continuously crawled and deduped skills from GitHub, so you stop hunting through repos manually.
• Use the upcoming trust score (0-100 from security, quality, license, maintenance and community signals) as a screening gate before any skill reaches a client-facing agent.

Why this is different

• Every skill version is pinned to a commit SHA — no silent upstream drift.
• Lift is benchmarked with and without the skill, in the same model, in a sandbox.
• Trust scores blend security, quality, license, maintenance, and community signals.
• Money never touches scores — Lift is always shown separately from ratings.
• Per-agent loadouts mean each agent only sees the skills and tools you chose.
• One MCP server equips Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents.
• Stay-current tracking re-scans and re-benchmarks skills when upstream changes land.
• An independent testing lab — not a skill marketplace with a conflict of interest.

SkillzBench is pre-launch and actively being built — join the waitlist now to be first in line when Discover, Verify, Benchmark, and Equip go live.

I've been burned installing SKILL.md packages blind from GitHub. Knowing every version gets pinned to a commit SHA, security-scanned, and license-checked before it touches my agents — with a trust score I can actually explain to our security reviewer — is exactly what agent skills have been missing. — Dana Whitfield

Every vendor shows a great demo. The promise of a real benchmark — same model, sandbox, with and without the skill, Lift published in percentage points and always separate from ratings — is the first honest way I've seen to prove a skill actually helps before it goes into production. — Marcus Oyelaran

Per-agent loadouts where the agent only sees what you toggled is the cleanest answer to tool-use chaos I've come across. One MCP server, one control panel, and each agent gets exactly the skills and tools it should. I joined the waitlist the day I read about it. — Priya Raghunathan

Frequently asked questions

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

SkillzBench is building a Benchmark pillar that tests every skill in a sandbox with the same model, running tasks both with and without the skill, then publishing the Lift in percentage points. Lift is always shown separately from trust ratings, so performance claims stay independent from scores.

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

Launch scope is instruction-only skills — SKILL.md plus text references. Script-bearing skills are listed link-out only, so you can see they exist but SkillzBench won't install or benchmark them at launch.

How do I keep agents like Claude Code and Cursor from seeing tools and skills they shouldn't?

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 — a direct answer to tool-use coordination policy headaches.

What happens when a skill I rely on gets updated upstream on GitHub?

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 what changed and whether the trust score moved.

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

Marketplaces list skills; SkillzBench tests them. It's an independent lab — every skill is deduped from continuous GitHub crawling, validated, license-checked, security-scanned, quality-judged, and benchmarked for Lift. Money never touches scores, so ratings can't be bought.