Do Skills Work Offline?

Why the Offline Question Matters Right Now

You install a folder with a SKILL.md file, your agent gets smarter, and everything works — until the Wi-Fi drops. Suddenly you're wondering whether that skill you trusted is actually running, or whether it was quietly depending on a live connection the whole time. Almost no documentation answers this, and that gap matters more than you might think.

AI agents have crossed from experiment to infrastructure. According to the 2025 Cisco AI Readiness Index, 83% of organizations plan to deploy agentic AI systems, and LangChain's State of Agent Engineering survey found 57.3% of respondents already run agents in production. These aren't prototypes in a sandbox — they're workflows people depend on.

At the same time, agent skills have become a cross-vendor open standard. What started as an Anthropic convention has been adopted by OpenAI's Codex, Google's Gemini CLI, GitHub Copilot, and Cursor, per a practitioner analysis of the skills ecosystem. A skill you install into Claude Code today may be the same package a colleague runs in Cursor tomorrow.

That mainstream status raises the stakes on questions nobody bothered to ask when skills were a niche curiosity:

• Does the skill's knowledge live locally, or does it depend on a live service?
• Can the workflow a skill orchestrates survive without connectivity?
• What happens when a skill bundles scripts that reach for the network?

The answer matters because skills aren't cosmetic. Research on SkillsBench found curated skills raise agent pass rates by 16.2 percentage points on average — while self-generated ones actually degrade performance by 1.3 points. A skill that silently fails offline isn't a minor inconvenience; it's a reliability hole in a system you're depending on for real work.

There's a security dimension too. The same research documents the ClawHavoc campaign, in which nearly 1,200 malicious skills infiltrated a major agent marketplace, exfiltrating API keys and credentials. SkillzBench is being built precisely for this moment — a free, independent directory where every skill is version-pinned, security-scanned, and benchmarked with and without the skill, so you know what you're installing before your agent ever loads it.

So before you trust a SKILL.md package in a plane, a client's air-gapped environment, or just a flaky hotel network, you need to understand where the offline boundary actually sits. The short version: the knowledge is local, but the workflow may not be.

Knowledge vs. Capability: What a Skill Actually Is

The honest answer starts with what a skill physically is. A skill is not a running service — it's a folder containing a SKILL.md file with instructions, plus optional scripts and reference files, stored locally and versionable in Git. Because the knowledge lives in plain local files, reading it requires no network connection at all.

That architecture creates a crucial distinction between knowledge and capability. As one practitioner analysis puts it, MCP gives an agent a capability — live access to a database, browser, or external system — while a skill gives it knowledge: your process, your domain rules, your scripts. A skill can even instruct the agent on which MCP tools to call and in what order. The skill itself is static; the workflow it orchestrates may not be.

The loading design reinforces how lightweight these packages are. Thanks to progressive disclosure, only about 100 tokens — the skill's name and description — load at startup, with the full body read only when a task actually matches. There's no hosting, no authentication token management, no protocol upkeep. This is what makes skills structurally offline-friendly in a way that live MCP connections are not.

So where does connectivity actually enter the picture? Three places:

• The agent itself: most agents run on cloud LLMs, so the "brain" executing the skill typically needs an API connection even when the skill files sit on your disk.
• MCP-dependent workflows: any skill that calls MCP tools or external systems needs those live connections to function.
• Script execution: skills can contain executable scripts, and if those scripts reach out to APIs or services, connectivity becomes part of the skill's real requirements.

None of this changes why skills matter. Research on the SkillsBench evaluation found that curated skills raise agent pass rates by 16.2 percentage points on average — while self-generated skills actually degrade performance by 1.3 points. A curated skill, the researchers note, is more reliable than an ad-hoc plan generated on the fly.

The practical takeaway, then, is a split verdict: mostly yes for the knowledge, it depends for the workflow. The SKILL.md instructions and bundled scripts will sit on your machine whether or not you're online. Whether the agent can do anything useful with them depends on the model it runs on and the tools the skill orchestrates.

Because the boundary varies skill by skill, it pays to know what you're installing before you rely on it. That's the problem SkillzBench is building for: a free directory where every skill is version-pinned, security-scanned, and benchmarked with a published Lift score, so you can see exactly what a skill adds — and what it needs — before it ever touches your agent.

The Catch: Which Skills You Install Matters More Than Where They Run

Skills can run offline — but only if you pick the right ones.

Here's the uncomfortable truth the hype cycle tends to skip past: research published on arXiv found that curated skills lift agent pass rates by an average of 16.2 percentage points, while self-generated skills actually degrade performance by 1.3 points. The difference between "skills make my agent smarter" and "skills made my agent worse" comes down to curation, not connectivity.

That gap matters more than any offline-versus-online distinction. A smaller model equipped with well-vetted skills can outperform a larger model running without them — which means your choice of skill is effectively a choice of model tier.

But the ecosystem surrounding that 16.2-point opportunity is genuinely risky. The same academic work documents the ClawHavoc campaign, in which nearly 1,200 malicious skills infiltrated a major agent marketplace and exfiltrated API keys, cryptocurrency wallets, and browser credentials. Separately, published research cited across the security community estimates that roughly 26% of skills carry at least one vulnerability. A skill is a folder of instructions plus optional scripts — and a script is code execution. Installing one is closer to installing a software package than to adding a browser bookmark.

That framing changes how you should evaluate any skill before it touches your agent:

• Read the SKILL.md and every script before installation — treat it like reviewing a dependency, not a settings toggle.
• Pin every version to a specific commit SHA so a maintainer (or attacker) can't silently swap the contents under you.
• Check the license, the maintenance history, and whether the skill has been independently benchmarked with and without it.
• Separate quality signals from security signals — a popular skill can still be vulnerable, and a clean skill can still be useless.

The offline question has a quiet corollary: because skills are static files, they're easy to snapshot, hash, and audit locally before an agent ever touches them. That auditability is exactly what makes a benchmarked, verified directory more valuable than a raw search.

Practitioners tracking the space now describe the standard posture as "treat installing a skill like installing software." For teams building agents today, that means the install step — not the runtime environment — is where the real risk and the real reward both live.

How to Build an Agent Setup You Can Trust (Online or Off)

Read the SKILL.md and its scripts before you install anything. That single habit separates a useful agent setup from a risky one, because a skill is just a folder with a SKILL.md file plus optional scripts and reference files — and it runs with the same permissions you do. Treat installing a skill like installing software, and only take it from sources you trust.

The stakes are not theoretical. Published research cited in the broader skills ecosystem found that roughly 26% of skills carry at least one vulnerability, and the ClawHavoc campaign saw nearly 1,200 malicious skills infiltrate a major agent marketplace, exfiltrating API keys, crypto wallets, and browser credentials. A malicious skill is effectively a self-installed prompt injection.

The payoff for getting this right is measurable, and it depends far more on curation than on availability. SkillsBench data shows that curated skills raise agent pass rates by 16.2 percentage points on average, while self-generated skills actually degrade performance by 1.3 points. A smaller model with curated skills can outperform a larger model without them.

A setup you can trust rests on four habits:

• Read before you run — open SKILL.md and every script, and check what the skill asks the agent to do.
• Prefer verified skills with a known origin and a pinned version over ad-hoc ones pulled from anywhere.
• Pair the knowledge half with the capability half deliberately: skills carry your process, while MCP supplies live access to systems.
• Re-check installed skills when they update, since an upstream change can quietly alter behavior.

That pairing is where most teams stop short. Skills and MCP solve different problems — one gives the agent knowledge, the other gives it a capability — so choosing both on purpose beats bolting on whatever is trending. SkillzBench is being built as a free, independent directory that crawls and dedupes skills, pins every version to a commit SHA, security-scans and license-checks each one, and benchmarks every skill in a sandbox with and without it, publishing the Lift in percentage points. A trust score from 0 to 100 covers security, quality, license, maintenance, and community signals, and Lift always stays separate from ratings. A free MCP connector with per-agent loadouts lets each agent see only the skills and tools you toggle on.

Everything on the platform is free — directory, MCP connector, benchmarks, and alerts — and money never touches scores. Join the waitlist at skillzbench.com to get early access when it opens.