Can Agent Skills Access the Internet? Requirements Explained

The Confusion: Skills Look Like Plugins, But They're Just Instructions

When you install an agent skill and it suddenly fetches live data or hits an API, it feels like you just plugged in a new integration. That instinct is understandable — and it's exactly where most confusion about skills and internet access begins.

A skill is a folder containing a SKILL.md markdown file with plain-English instructions and, optionally, a few scripts. As IBM Developer puts it, there are no servers to run and no APIs to configure. A skill does not automatically create an authenticated channel to a hosted service; it can only take side effects through tools the agent already has permission to use.

That distinction matters because the thing that actually reaches the internet is a different artifact entirely. Architecturally, skills answer "how should I do this task?" while MCP servers answer "how do I access that external service?" — MCP servers expose callable tools that perform live, authenticated execution against remote systems. Skills package reasoning; MCP servers perform the fetching, querying, and side effects.

The confusion compounds because skills can direct an agent toward the network, even though they have no built-in network capability of their own. Three paths show up in practice:

• The agent's existing tools and permissions — a skill simply tells the agent which of its built-in tools to reach for
• Bundled scripts that make API or network calls using the host platform's access (skills that bundle executable scripts run this risk most acutely)
• MCP servers, which provide the governed, controlled interface for external access with credentials kept server-side

This is why burying network instructions or credentials inside a SKILL.md file is an explicit anti-pattern. If a capability depends on live, authenticated state, give it a real controlled interface — an MCP server or an already-approved tool — instead of prose that merely hopes the agent has network reach. The same skill can behave completely differently depending on where it runs: Claude API skills execute in a sandboxed container with no network access at all, while Claude Code skills inherit full network access matching any program on the user's machine.

Getting this wrong isn't theoretical. A large-scale study of 31,132 unique skills found 26.1% carry at least one vulnerability, with data exfiltration the most common pattern at 13.3% — and skills bundling executable scripts are 2.12× more likely to contain vulnerabilities than instruction-only ones. Treating a skill like a plugin you blindly trust is how benign-seeming downloads turn into real incidents.

Knowing which skills are instruction-only, which bundle risky scripts, and which quietly reach for the network is exactly the gap SkillzBench is building to close — its free directory security-scans, version-pins, and benchmarks every skill so you can see what a skill actually does before you equip your agent with it.

ctaText: Browse free, security-scanned agent skills on SkillzBench socialProofText: SkillzBench pins every skill to a commit SHA, security-scans it, and benchmarks its real Lift — so you know what you're installing before your agent does.

The Real Answer: It Depends on Your Platform and the Skill's Contents

If you've read two different articles about agent skills and the internet and walked away with opposite answers, both were probably right. The honest answer is that it depends on your platform and what's inside the skill — and understanding that split is the key to using skills safely.

Start with the platform, because network access varies dramatically across Claude's surfaces. According to Anthropic's official documentation, skills running through the Claude API live in a sandboxed container with no network access at all. Claude Code skills, by contrast, have full network access — the same as any other program on your computer. On claude.ai, access varies depending on user and admin settings. The identical skill can behave completely differently depending on where you run it.

Now the skill itself. A skill is fundamentally an offline instruction package: a SKILL.md file with plain English guidance, plus optional bundled scripts. As IBM Developer explains, skills work offline and have no servers to run or APIs to configure. They don't inherently create network connections — they shape how an agent reasons about a task.

So how do the "skills make network calls" claims square with this? Three documented mechanisms:

• The agent's existing tools. A skill can only perform side effects through tools the agent already has — so on Claude Code, where the agent has full network permissions, skill instructions can direct it to fetch URLs or call APIs.
• Bundled scripts. Skills can include code the agent executes at its discretion. Red Hat practitioners describe real skills connecting to data servers and making script-based GitHub API calls this way.
• MCP servers. The recommended controlled interface for external access — skills answer "how should I do this task?" while MCP servers handle "how do I reach that external service?"

This distinction matters because buried network access is a real attack vector. A large-scale security study of 31,132 unique skills found 26.1% contain at least one vulnerability, with data exfiltration the most prevalent pattern at 13.3%. Skills bundling executable scripts were 2.12× more likely to contain vulnerabilities than instruction-only skills. In December 2025, researchers demonstrated a benign-looking "GIF Creator" skill that silently deployed MedusaLocker ransomware.

Anthropic's guidance is blunt: pay attention to instructions or code within a skill that directs Claude to connect to untrusted external sources, and thoroughly audit any skill from a less-trusted source before use. The consensus across sources is clear — internet access should flow through governed, controlled interfaces like MCP servers, not hide inside skill prose.

Because open marketplaces aggregate community skills without mandatory security vetting, checking what a skill actually does before installing it is now essential. That's the gap SkillzBench is building to close: a free directory that pins every skill to a commit SHA, security-scans it, and benchmarks it in a sandbox — so you can see what each skill does and whether it actually helps before it ever touches your agent.

Why This Matters: The Security Cost of Networked Skills

Every time you approve a new agent skill, you may be handing it far more access than you realize. The security data on networked skills paints a picture that every AI-curious professional — technical or not — should see before clicking "install."

A large-scale analysis of 31,132 unique skills found that 26.1% carry at least one vulnerability across 14 distinct patterns. Data exfiltration appears in 13.3% of skills, making it the single most common risk — meaning roughly one in seven skills contains code or instructions that can quietly send your data somewhere else.

The risk multiplies fast when skills bundle executable scripts. Those skills are 2.12 times more likely to contain vulnerabilities than instruction-only skills, a statistically significant gap that should change how you evaluate anything that ships with code alongside its SKILL.md file.

Real attacks have already moved past theory. In December 2025, Cato CTRL researchers demonstrated a "GIF Creator" skill that appeared benign but silently downloaded and executed MedusaLocker ransomware. A separate campaign weaponized Claude Code with malicious MCP servers to harvest credentials and move laterally through networks — with 80–90% of its tactical operations running fully autonomously.

Then there is the consent gap, which turns a single click into a standing agreement. Once a user approves a skill, it gains persistent permissions to read files, write files, download code, and open network connections without further prompts. You approve once; the skill keeps those permissions for as long as it stays installed.

Here is what the research flags as the highest-risk behaviors to watch for:

• Skills that fetch content from external URLs — fetched data can carry malicious instructions back into the agent's context
• Skills that bundle executable scripts alongside their instructions
• Skills with embedded credentials or undocumented network calls buried in prose
• Skills from open marketplaces with no mandatory security vetting before listing
• Security-themed skills, which carry the highest raw vulnerability rate at 67.4%

The market is responding: the OWASP Agentic Skills Top 10 now provides the first comprehensive security framework for this ecosystem, and vetted directories are emerging to fill the gap left by unmoderated repositories. SkillzBench is building exactly this kind of layer — version pinning, security scanning, and sandboxed benchmarking so you can see what a skill actually does before your agent runs it.

The pattern is clear: treat every networked skill as software you are installing on a machine that holds your data. Audit before you approve, prefer skills from sources that scan and pin versions, and route any genuine internet access through a governed interface — not through instructions hidden in a markdown file.

The Right Way to Give Skills Internet Access: MCP, Not Buried Instructions

If a skill needs to reach the internet, the architecture matters more than the ambition. The expert consensus is clear: put external access in an MCP server with governed controls, and keep skills as pure instruction packages — never bury credentials or network calls inside a SKILL.md file.

The distinction is simple. Skills answer "how should the agent think about this task?" while MCP servers answer "how does the agent actually reach that external service?" As one practitioner guide puts it, don't put credentials or undocumented network instructions into a skill and call it an integration — if a capability depends on authenticated, live data, it needs a real controlled interface.

Why is this so strongly advised? Because burying network access in a skill is a documented attack vector. A large-scale security study of 31,132 agent skills found that 26.1% contain at least one vulnerability, with data exfiltration the most prevalent pattern at 13.3%. Skills that bundle executable scripts are 2.12× more likely to be vulnerable than instruction-only skills, and researchers demonstrated a benign-looking "GIF Creator" skill that silently deployed ransomware.

MCP servers, by contrast, offer structural safeguards that prose instructions cannot:

• Credentials stay server-side, never pasted into skill instructions where they can leak
• Tool calls are schema-validated and logged, so every external action is auditable
• Access is governed per agent, rather than granted implicitly through file permissions
• Fetched content flows through a controlled interface instead of raw URLs that may carry malicious instructions

That last point matters more than most teams realize. Anthropic's own platform documentation warns that skills fetching data from external URLs pose particular risk, because fetched content may contain malicious instructions the agent then follows.

There's also a build-order question: when do you even need MCP? The recommended philosophy across sources is to start with skills and fixture data, validate the workflow offline, and only add an MCP server once the repeated external operation and its contract are stable. IBM's guidance follows the same path — begin with skills, prove the value, then connect external tools incrementally. This keeps costs down too: MCP tools consume 2K+ tokens even when idle, while skills cost roughly 100 tokens at discovery.

For teams evaluating skills from the community, vetting is the other half of the equation. That's the gap SkillzBench is building to close: a free directory that pins every skill to a commit SHA, runs security scans, and benchmarks each skill in a sandbox so you can see its measured Lift before it ever touches your agent — with a free MCP connector that lets you toggle exactly which skills and tools each agent can see.

The rule of thumb, then: skills teach, MCP connects, and nothing network-related lives inside a skill's prose.

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Your Action Plan: Vet Skills Before They Touch Your Network

Before you install another skill, pause. The difference between a helpful instruction set and a security breach often lies in how network access is handled.

Most users assume skills are harmless text files. In reality, a skill can direct an agent to fetch external data, creating a potential vector for malicious payloads. Recent large-scale analysis of over 31,000 unique skills revealed that 26.1% contain at least one vulnerability. This is not a theoretical risk; it is a documented reality in the current ecosystem.

Your first step is to verify your platform’s specific network policy. The same skill behaves differently depending on where it runs. On the Claude API, skills execute in a sandboxed container with no network access. However, on Claude Code, skills inherit full network permissions, behaving like any other program on your machine. Understanding this distinction is critical before you approve any new capability.

Once you know your baseline, audit every skill that attempts to fetch external URLs. Anthropic explicitly warns that skills fetching data from external sources pose particular risk because the fetched content may contain malicious instructions. Do not trust the skill’s description; look at the underlying code.

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Instead of relying on raw GitHub repositories, use vetted directories that enforce strict standards. This means pinning versions to specific commit SHAs, ensuring that the skill you tested is the exact skill you deploy. It also involves running security scans and sandboxed benchmarks before the skill ever touches your production environment.

This is precisely the approach Anthropic recommends for maintaining trust in agent workflows. They advise using skills only from trusted sources and thoroughly auditing less-trusted ones before use. By treating skills as software rather than static documents, you mitigate the risk of privilege escalation and data leakage.

SkillzBench is building a free, independent directory that automates this vetting process. We are developing a system that continuously crawls skills, pins them to commit SHAs, and runs them through sandboxed benchmarks. Each skill receives a 0–100 trust score based on security, quality, and maintenance signals, giving you a clear, objective metric before you install.

You do not need to guess if a skill is safe. You need evidence. Join the SkillzBench waitlist to access our upcoming directory and MCP connector. We will provide the benchmarks and security scans you need to deploy agent skills with confidence, ensuring your network remains secure while your agents become more capable.