context safety score
A score of 37/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
github api error
Could not fetch GitHub metadata: GitHub API returned 404: {"message":"Not Found","documentation_url":"https://docs.github.com/rest/repos/repos#get-a-repository","status":"404"}
typosquat
Generic high-value name 'frontend-design' with zero community signals (0 stars, 0 forks, 0 contributors), no identifiable owner (empty owner/repo fields), unknown account age, empty SKILL.md, and no registry listing. The skill_description is 'width=device-width, initial-scale=1' — an HTML viewport meta tag value, not a real description — suggesting metadata was scraped from HTML or is a deliberate placeholder. This appears to be namespace squatting on a desirable generic name. (location: metadata.json)
scope violation
SKILL.md is completely empty — the skill declares no capabilities, no parameters, no documentation whatsoever. An agent installing this skill has zero information about what it actually does, yet it claims 7.69M installs. The mismatch between claimed popularity and complete absence of content is deceptive. The install count is not credible given 0 stars, 0 forks, 0 contributors, and no registry listing. (location: SKILL.md)
curl https://api.brin.sh/skill/smithery%2Fai%2Ffrontend-designCommon questions teams ask before deciding whether to use this skill in agent workflows.
smithery/ai/frontend-design currently scores 37/100 with a suspicious verdict and low confidence. The goal is to protect agents from high-risk context before they act on it. Treat this as a decision signal: higher scores suggest lower observed risk, while lower scores mean you should add review or block this skill.
Use the score as a policy threshold: 80–100 is safe, 50–79 is caution, 20–49 is suspicious, and 0–19 is dangerous. Teams often auto-allow safe, require human review for caution/suspicious, and block dangerous.
brin evaluates four dimensions: identity (source trust), behavior (runtime patterns), content (malicious instructions), and graph (relationship risk). Analysis runs in tiers: static signals, deterministic pattern checks, then AI semantic analysis when needed.
Identity checks source trust, behavior checks unusual runtime patterns, content checks for malicious instructions, and graph checks risky relationships to other entities. Looking at sub-scores helps you understand why an entity passed or failed.
brin performs risk assessments on external context before it reaches an AI agent. It scores that context for threats like prompt injection, hijacking, credential harvesting, and supply chain attacks, so teams can decide whether to block, review, or proceed safely.
No. A safe verdict means no significant risk signals were detected in this scan. It is not a formal guarantee; assessments are automated and point-in-time, so combine scores with your own controls and periodic re-checks.
Re-check before high-impact actions such as installs, upgrades, connecting MCP servers, executing remote code, or granting secrets. Use the API in CI or runtime gates so decisions are based on the latest scan.
Learn more in threat detection docs, how scoring works, and the API overview.
Assessments are automated and may contain errors. Findings are risk indicators, not confirmed threats. This is a point-in-time assessment; security posture can change.
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