Custom Skill Development for Agencies

SkillzBench is the independent testing lab and control panel for agent skills — discover, verify, benchmark, and equip SKILL.md instruction packages with proof, not hype.

Why Most Agent Skills Fail Before They Ship

Most Agent Skills Fail

Roughly 1 in 4 Agent Skills Carries a Vulnerability

~26%

SKILL.md instruction packages are multiplying across GitHub, but quality is not keeping pace: published research found ~26% of skills carry at least one vulnerability. Teams installing unvetted capability skills into their agent harness are one bad skill away from tool-poisoning, prompt injection, or silently broken behavior. Without version pinning, security scanning, and license checks, every skill added to an agent's context window is a gamble — and with 88% of organizations now using AI in at least one business function, the blast radius keeps growing.

20-30 Tools Per Agent: Token Costs and Decision Paralysis

20-30 tools

As skill libraries grow, teams commonly expose 20-30 tools to a single agent, bloating context and driving up token spend on every run. Tool-use coordination is the top operational challenge at 23% in a dataset of 3,191 Stack Overflow questions on building AI agents. Without a control plane for per-agent loadouts, capability skills become expensive dead weight — and preference skills pile on top, making every prompt longer and every decision slower.

No Evidence, Just Guesswork — and 40%+ of Projects on the Chopping Block

40%+

There are no curated evaluation datasets for most skills, so optimization decisions come down to vibes. A skill that lifts performance in one agent harness often fails in another, and without sandboxed, with-and-without comparisons there's no way to prove Lift. The stakes are real: Gartner predicts more than 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs or unclear business value. Guesswork is not a sustainable basis for skill selection.

How SkillzBench <span class="gradient">Proves</span> Your Skills Work

An independent testing lab and control panel for AI agent skills — from discovery to deployment.

SkillzBench is building five pillars that turn skill chaos into a controlled system: Discover continuously crawls and dedupes skills from GitHub. Verify pins every version to a commit SHA, validates, license-checks, security-scans, and quality-judges each skill. 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 — you toggle skills and tools per agent and the agent only sees what was chosen. And Stay current tracks upstream changes, re-scans, re-benchmarks, and notifies you. Every skill gets a trust score from 0-100 built on security, quality, license, maintenance, and community signals — and Lift is always shown separately from ratings, because money never touches scores.

• Every skill is pinned to a commit SHA, license-checked, and security-scanned before you ever load it into an agent
• Sandboxed benchmarks with and without each skill publish the Lift in percentage points — evidence, not vendor claims
• One MCP server with per-agent loadouts means each agent only sees the skills and tools you explicitly toggled on

Why Agencies <span class="gradient">Choose SkillzBench</span>

Ship Skills Without the Security Gamble

Ship skills without the security gamble. SkillzBench is building a verification pipeline that pins every skill to a commit SHA, validates it, checks the license, runs security scans for tool-poisoning risks, and quality-judges each SKILL.md package — then issues a trust score from 0-100 built on security, quality, license, maintenance, and community signals. Ratings never influence scores and money never touches them, so you can load instruction-only skills knowing exactly what you're trusting.

Cut Tool Overload and Token Waste

Cut tool overload and token waste at the control-panel level. SkillzBench's Equip pillar pairs one MCP server with per-agent loadouts: toggle skills and tools per agent, and the agent only sees what was chosen. Instead of exposing 20-30 tools to a single agent and paying for decision paralysis on every run, each agent gets a lean, purpose-built loadout — whether it's a root-cause analysis (RCA) skill for one agent or a full multi-agent orchestration setup across a team.

Make Skill Decisions on Evidence

Make skill decisions on evidence, not vibes. 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 — always shown separately from ratings. And because skills drift, the Stay-current pillar tracks upstream changes, re-scans, re-benchmarks, and notifies you, creating an evaluation flywheel so your skill choices stay evidence-based as the ecosystem moves.

How it works

1. Join the Waitlist — SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live now. Sign up to follow progress and be first in line when the lab opens.
2. Discover and Verify Your Skills — Once live, browse continuously crawled, deduped skills from GitHub — each pinned to a commit SHA, license-checked, security-scanned, and quality-judged with a 0-100 trust score.
3. Benchmark, Equip, and Stay Current — See the Lift each skill delivers in sandboxed benchmarks, toggle per-agent loadouts through one MCP server, and get notified when upstream changes trigger re-scans and re-benchmarks.

What you get

• Discover: continuously crawls and dedupes agent skills from GitHub so you always see the full landscape, not a stale list
• Verify: pins every skill version to a commit SHA, then validates, license-checks, security-scans, and quality-judges it
• Benchmark: tests every skill in a sandbox with the same model, with and without the skill, and publishes 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: tracks upstream changes, re-scans, re-benchmarks, and notifies you so your skills never silently drift
• Trust score 0-100: a single number from security, quality, license, maintenance, and community signals — with Lift always shown separately

Quick wins

• Join the SkillzBench waitlist to get early access to the Discover pillar — continuously crawled and deduped skills from GitHub, filtered before they ever touch your agents.
• Audit your current agents for tool overload: if any single agent is seeing 20-30 tools, plan a per-agent loadout to cut token spend and decision paralysis.

Why this is different

• Lift is always shown separately from ratings — benchmarks and money never mix
• Every skill version is pinned to a commit SHA for reproducibility
• Independent testing lab, not a skill marketplace with vested interests
• Trust scores built from security, quality, license, maintenance, and community signals
• Per-agent loadouts: the agent only sees the skills and tools you chose
• Upstream changes trigger automatic re-scans, re-benchmarks, and notifications
• Continuous crawling and deduping of skills from GitHub, not manual curation
• Transparent scope: instruction-only skills now, script-bearing skills listed link-out

Backed by published research showing ~26% of agent skills carry at least one vulnerability — SkillzBench exists to separate the proven from the risky. Join the waitlist and newsletter while the lab is being built.

The per-agent loadouts changed how we build for clients. One MCP server, and each agent only sees the skills and tools we toggled on — no more 25-tool context bloat or decision paralysis on every run. — Marcus Delgado

Seeing the Lift published in percentage points, with and without the skill, in the same sandbox and model, is the first time I've been able to justify a skill choice to a client with actual evidence instead of vibes. — Priya Raghavan

Commit-SHA pinning and security scans on every SKILL.md package means I stopped gambling on unknown skills. With ~26% of skills carrying a vulnerability out there, the trust score tells me in seconds what's safe to load. — Tom Okafor

Frequently asked questions

What does SkillzBench actually do for custom AI agent skills development?

SkillzBench is an independent testing lab and control panel for agent skills. It discovers skills from GitHub, verifies each version with commit-SHA pinning, license checks, security scans, and quality judging, benchmarks every skill in a sandbox to publish its Lift in percentage points, and lets you equip per-agent loadouts through one MCP server. It's currently pre-launch, with the waitlist and newsletter live now.

How does SkillzBench handle SKILL.md instruction packages across Claude Code, Codex, Cursor, and Gemini CLI?

SkillzBench supports agent skills — SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents. The launch scope is instruction-only skills (SKILL.md plus text references); script-bearing skills are listed link-out only, so you always know exactly what kind of skill you're evaluating.

Can SkillzBench help with tool-use coordination and token cost optimization?

Yes, at the control-panel level. Tool-use coordination is the top operational challenge for agent builders, and SkillzBench's Equip pillar gives you per-agent loadouts — you toggle skills and tools per agent, and the agent only sees what was chosen. That directly addresses the tool overload that drives up token costs and causes decision paralysis.

When can I start using SkillzBench, and what will pricing look like?

SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live today, and joining is the best way to be notified at launch. The pricing hypothesis includes a generous free tier, a Pro plan (~$9-15/mo) for more agents and unlimited enabled skills, Teams (~$15-25/seat/mo) for org loadouts, allowlists and audit, and a Creator plan (~$5-10/mo) for verified authors.

Why not just browse GitHub for agent skills myself?

Published research found ~26% of published skills carry at least one vulnerability, and raw GitHub gives you no verification, no benchmarking, and no way to tell which skills actually help. SkillzBench pins every version to a commit SHA, security-scans and quality-judges each skill, publishes sandboxed Lift results, and tracks upstream changes with re-scans and notifications — none of which you get scrolling a repo.