context safety score
A score of 37/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 6 secret pattern match(es) in repository files
typosquat
Skill named 'elysiajs' from 'elysiajs/skills' impersonates the popular ElysiaJS Bun web framework. The real ElysiaJS has thousands of GitHub stars; this skill has 50 stars, 1 contributor, is not listed on the registry, org is not verified, has no license, and SKILL.md is completely empty — providing zero actual functionality. The claimed 7.69M install count is not credible for an unlisted, empty skill. (location: metadata.json (skill_name: elysiajs, full_name: elysiajs/skills))
scope violation
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag value, not a legitimate skill description. Combined with an empty SKILL.md, this indicates the skill metadata was fabricated or scraped from a web page rather than representing a genuine skill. The skill registers itself with no documented capabilities or tools, making its actual purpose opaque and potentially deceptive. (location: metadata.json (skill_description field))
curl https://api.brin.sh/skill/elysiajs%2Fskills%2FelysiajsCommon questions teams ask before deciding whether to use this skill in agent workflows.
elysiajs/skills/elysiajs 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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