Custom Skill Development for Researchers

SkillzBench is the independent testing lab and control panel for agent skills — verified, benchmarked, and delivered as custom SKILL.md packages built for your Claude Code, Codex, Cursor, and Gemini CLI agents.

The Skill Boundary Problem Is Burning Your Agent Budget

Burning Your Agent Budget

Agents Without Skill Boundary Awareness Burn Your Entire Interaction Budget

80%

Agents lacking skill boundary awareness waste tokens, time, and API calls attempting tasks beyond their capability until the interaction budget runs out. This isn't a marginal inefficiency — research on skill boundary exploration shows handling it correctly delivers a 20% performance gain and an 80% efficiency improvement. In an agentic AI market projected to grow from USD 7.6 billion in 2025 to USD 182.9 billion by 2033, every failed exploration is inference-time compute you paid for and got nothing back. Proper context engineering — knowing exactly which unit skills and composite workflows an agent can execute — is the difference between a demo and a production workload.

Roughly of Agent Skills Ship With At Least One Vulnerability

Agent skills — SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI and chat-app agents — have proliferated across GitHub with zero vetting. Published research found roughly of skills carry at least one vulnerability, and security is the top adoption challenge for 53% of leaders and 62% of practitioners. Copying an unvetted SKILL.md into your agent's context means injecting untrusted instructions into every tool call it makes. With 88% of organizations reporting confirmed or suspected agent security incidents, grabbing skills off GitHub without license checks, security scanning, and version pinning is a gamble with your entire agent control plane.

Tool-Use Coordination Policies Are the #1 Operational Challenge at 23%

23%

An empirical study of 3,191 Stack Overflow questions found tool-use coordination policies are the top operational challenge, cited in 23% of cases — configuring when and how agents invoke tools, sequencing parallel use, and avoiding conflicts. When every agent sees every skill and every tool, you get conflicting interaction contracts, duplicated tool invocations, and debugging sessions that never end. The leap from impressive demo to production remains the dark underbelly of agentic AI: 62% of organizations are experimenting with agents but only 23% have scaled them — a 39-point pilot-to-production gap, and Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027.

How SkillzBench <span class="gradient">Takes the Guesswork</span> Out of Agent Skills

Custom skill development backed by an independent testing lab — every skill verified, benchmarked, and matched to your agent's loadout.

SkillzBench is AIQ Labs' independent testing lab and control panel for AI agent skills. We continuously discover and dedupe skills from GitHub, pin every version to a commit SHA, and run license checks, security scans, and quality judgment before anything reaches your agents. Every skill is benchmarked in a sandbox with the same model, with and without the skill, so you see the Lift in percentage points — never a marketing claim. For teams that need skills tailored to their workflows, our custom skill development service crafts instruction-only SKILL.md packages designed around your agents, then validated and benchmarked through the same rigorous pipeline.

• Custom SKILL.md instruction packages engineered for your Claude Code, Codex, Cursor, or Gemini CLI agents — instruction-only scope, no hidden scripts
• Every skill pinned to a commit SHA and passed through license-check, security-scan, and quality-judge validation before delivery
• Sandbox benchmarking with and without the skill publishes the Lift in percentage points, so you know exactly what each skill adds

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

Skills Proven, Not Promised

Stop hoping a skill helps. SkillzBench benchmarks 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, with money never touching scores. Every version is pinned to a commit SHA, so the number you see is the number you get. That's the evaluation flywheel discipline that addresses why Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, and why only 23% of experimenting organizations have actually scaled their agents.

Security Baked In From the Start

With 88% of organizations reporting confirmed or suspected agent security incidents, blind skill installation is indefensible. SkillzBench's Verify pillar runs every skill through validation, license checks, security scanning, and quality judging, then pins the version to a commit SHA. A trust score from 0-100 combines security, quality, license, maintenance and community signals — so you know a skill's risk profile before it ever touches your agent's context. Upstream changes are tracked, re-scanned, and re-benchmarked automatically, so a skill that was safe last month can't silently become a liability.

Right Skill, Right Agent, Every Time

Per-agent loadouts mean each agent only sees the skills and tools you toggled on — nothing else. One MCP server serves your whole stack, and your Claude Code, Cursor, and Codex agents each get a deliberate, curated context instead of a firehose. That's tool-use coordination policy by design, solving the exact chaos that 23% of developers cite as their top operational challenge, and it's how you close the 39-point pilot-to-production gap between the 62% experimenting with agents and the 23% who've actually scaled them.

How it works

1. Join the Waitlist — SkillzBench is actively being built — join the waitlist or newsletter to get early access and shape the launch of the testing lab and control panel.
2. Define Your Agent's Skills — Tell us which agents you run — Claude Code, Codex, Cursor, Gemini CLI, or chat-app agents — and what capabilities you need as custom instruction-only SKILL.md packages.
3. Deploy Verified, Benchmarked Skills — Receive custom skills that are version-pinned, security-scanned, and benchmarked with published Lift — loaded per agent so each one only sees what you chose.

What you get

• Discover: continuous crawling and deduplication of agent skills from GitHub, so you're never sifting through duplicate or abandoned packages
• Verify: every skill version pinned to a commit SHA, then validated with license checks, security scans, and quality judgment before it earns trust
• Benchmark: every skill tested in a sandbox with the same model, with and without the skill, publishing 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: upstream changes tracked with automatic re-scans, re-benchmarks, and notifications when a skill you rely on shifts
• Trust score 0-100 built from security, quality, license, maintenance, and community signals — with Lift always shown separately

Quick wins

• Pin every skill version to a commit SHA before deploying — so the skill you benchmarked is the skill your agent runs, even if upstream changes hours later.
• Toggle skills and tools per agent instead of loading everything: an agent that only sees what it needs stops burning interaction budget on out-of-boundary tasks.

Why this is different

• Independent testing lab — we measure skills, we don't sell hype
• Lift published in percentage points, always separate from ratings
• Money never touches trust scores — zero pay-to-rank
• Every skill version pinned to a commit SHA
• Per-agent loadouts: agents only see what you chose
• Instruction-only scope — script-bearing skills are link-out only
• Upstream changes tracked, re-scanned, re-benchmarked, and flagged
• Trust score 0-100 from security, quality, license, maintenance, community

Built by AIQ Labs as the independent testing lab for agent skills — trusted scoring where money never touches the numbers

The per-agent loadout concept is exactly what my team needed. Each Cursor agent only sees the skills and tools we toggled on, so tool conflicts stopped being a daily debugging session — and when upstream skills change, we get notified and re-benchmarked instead of finding out in production. — Marcus Ellery

I've installed enough random SKILL.md files from GitHub to know the ecosystem needs a vetting layer. SkillzBench's commit-SHA pinning, license checks, and security scanning mean I finally know what's actually inside a skill before it touches my agents — and the trust score gives me one number to reason about risk. — Priya Raghavan

Seeing Lift published separately from ratings, with money never touching scores, is the only benchmark model I'd actually trust when deciding which skills make it into my Claude Code setup. Same model, same sandbox, with and without the skill — that's the comparison I've always wanted. — Daniel Okafor

Frequently asked questions

What exactly does custom skill development from SkillzBench include?

We build instruction-only skill packages — SKILL.md plus text references — tailored to your agents on Claude Code, Codex, Cursor, Gemini CLI, or chat-app agents. Every custom skill goes through the same pipeline as listed skills: version pinning to a commit SHA, license checks, security scans, and quality judgment, plus sandbox benchmarking that publishes the Lift in percentage points.

How does SkillzBench handle script-bearing skills?

At launch, SkillzBench's scope is instruction-only skills (SKILL.md plus text references). Script-bearing skills are listed link-out only, meaning we point you to them but don't validate or bundle their executable content — a deliberate safety boundary while the ecosystem matures.

Why can't I just trust GitHub stars or vendor claims about agent skills?

Published research found roughly of agent skills carry at least one vulnerability, and security is the top challenge for both leaders (53%) and practitioners (62%) in enterprise agent adoption. SkillzBench replaces popularity signals with a trust score built from security, quality, license, maintenance, and community signals — and money never touches scores.

When can I actually use SkillzBench?

SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live now, but the product itself is not yet public. Joining the waitlist gets you early access to the testing lab, benchmark results, and the MCP server with per-agent loadouts as each pillar ships.

How is SkillzBench different from just browsing skill repositories myself?

Repositories give you raw, unvetted packages with no way to compare them. SkillzBench discovers and dedupes skills from GitHub, verifies each version, benchmarks every skill in a sandbox with the same model with and without the skill, and shows Lift separately from ratings — plus tracks upstream changes with re-scans and notifications so a skill you depend on never silently changes.