context safety score
A score of 28/100 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
Skill named 'mintlify' impersonates the well-known Mintlify documentation platform. Repository has 0 stars, 0 forks, 0 contributors, no license, empty owner/repo fields, unknown account age, not listed on any registry, and org is not verified. The 7.69M install count is inconsistent with zero community engagement, suggesting fabricated metrics or stats scraped from the real Mintlify package. This is a classic name-squat on a popular brand. (location: metadata.json)
scope violation
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML meta viewport tag value, not a legitimate skill description. Combined with an empty SKILL.md (0 lines of content), this indicates the skill metadata was scraped from a web page rather than authored as a genuine skill. The skill has no actual documented functionality, code, or purpose, yet claims the name of a popular platform. (location: metadata.json:skill_description and SKILL.md)
curl https://api.brin.sh/skill/mintlify%2Fcom%2FmintlifyCommon questions teams ask before deciding whether to use this skill in agent workflows.
mintlify/com/mintlify currently scores 28/100 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.