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To scan an agent skill, audit the entire bundle—not just its description—then trace what it can read, run, and send. Look for suspicious scripts, credential access, unexpected network destinations, and permissions that exceed the skill’s purpose. Use platform or CLI scanners as an additional check, but treat a clean result as limited evidence, not a safety guarantee.
What to inspect in an agent skill
A skill may include instructions, scripts, references, and other supporting files. Review the complete bundle before enabling it. Anthropic’s enterprise guidance says, “Never deploy Skills from untrusted sources without a full audit.” Anthropic’s secure-skills guidance and Help Center documentation describe risks that can arise from skill contents and their behavior.
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- Instructions and supporting files: Read all included files and note commands, tools, file paths, and external references.
- Scripts and file operations: Identify what runs and what it reads, writes, or changes. Check whether a command is necessary for the skill’s stated task.
- Network activity: Record destinations and inspect where links or redirects lead. A familiar-looking reference is not enough if it redirects somewhere unexpected.
- Data flows: Trace sensitive information from the point it is read to any later transmission or output. Instructions to encode, summarize, or expose file contents can still create a disclosure risk.
Reading a sensitive file is more concerning when the same skill can send information over a network or expose it in an output. Anthropic calls out the combination of file-reading and network-tool access, redirect destinations, and data-exfiltration patterns; OpenAI also warns that prompt injection can create data-exfiltration risks. OpenAI’s agent-skills guidance discusses those risks.
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How to check for secrets and unsafe permissions
Start with the skill’s claimed purpose, then compare it with the access its instructions and tools require. A permission summary is not itself a security boundary: limits must be enforced by the platform, sandbox, or host policy.
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- List the requested access. Note filesystem paths, commands, tools, network destinations, and any secrets the skill could reach.
- Look for credential-seeking behavior. Search the bundle for literal keys, tokens, passwords, private endpoints, and instructions to search credential files or environment variables.
- Trace any values it reads. Check whether credentials or other sensitive data are transformed, summarized, encoded, printed, or sent elsewhere.
- Compare access with purpose. Ask whether each permission is necessary for the task. Unexplained access to sensitive paths, broad shell execution, or unrelated network destinations deserves investigation.
- Constrain the runtime. Isolate workloads, limit outbound network access with allowlisting where possible, and keep application and third-party credentials outside the agent’s environment.
OpenAI’s sandbox guidance notes that agent-generated code can access files, credentials, and network resources made available to its environment. If a credential has been exposed, rotate or revoke it. No universal scanner is established here as able to detect every secret format, so a scan should not replace careful review and sound credential boundaries.
What built-in platform scans can and cannot establish
Anthropic documents organization-level scanning for eligible third-party skills and plugins when they are uploaded or edited. Its documented outcomes are pass, warn, and fail: failed items are blocked, warnings remain usable with a caution, and a pass means the scan found nothing concerning within its scope. Anthropic says most scans finish in about one to two minutes and results are cached; those are statements about that service, not performance guarantees for scanners generally. See the Help Center explanation of skill scanning.
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Coverage is not universal. Anthropic’s documentation lists exclusions including certain existing skills, skills shared through connected MCP servers, MCP servers and hooks, and organizations with specified data-handling configurations. Its enterprise documentation also says scanning does not cover skills uploaded through the Skills API. Check the current platform settings and the specific upload path to determine whether a skill is covered. Anthropic’s enterprise guidance and Help Center page describe these limits.
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A pass is not proof that a skill is harmless. Anthropic’s Help Center puts the limit plainly: “A pass result means the scan didn’t find that kind of threat. It isn’t a guarantee that a skill is safe in every respect, and it won’t catch a skill that behaves in ways you didn’t intend without being malicious.”
How to automate part of the review
The open-source skil project documents linting, validation, scanning, policy checks, and GitHub Action/SARIF integration. It describes offline malware and local cross-file semantic analyzers; some other sources and model-backed analysis are opt-in. This is an option to evaluate in a workflow, not proof that a skill is safe. The project warns: “Pattern, local semantic, and taint analysis can produce false positives and false negatives.”
Before relying on a CLI or CI scanner, independently verify its current version, release integrity, supported platform, policy fit, and any external dependencies. Compare approaches by what they cover (including scripts, external references, MCP, or hooks), when they run, whether they warn or block, which environments are excluded, and whether results are understandable and repeatable. Anthropic’s organization scanning and skil’s CLI/CI workflow have different deployment scopes; consult their current documentation rather than assuming they provide equivalent coverage.
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Why a clean scan still needs human review
Automated checks can miss behavior that depends on context, intent, or interactions across files. A 2026 empirical study, Malicious Agent Skills in the Wild: A Large-Scale Security Empirical Study, reported that 100% of the advanced attacks in its analyzed set used “shadow features” absent from public documentation. That is a finding about the study’s analyzed advanced attacks, not a prevalence estimate for agent skills overall. The authors also reported that 93.6% of the malicious skills discussed were removed within 30 days after responsible disclosure; that figure applies to the study’s scope, not to all malicious skills. The study is available through arXiv.
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