context safety score
A score of 38/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 11 secret pattern match(es) in repository files
supply chain
Install count (7.69M) is wildly inconsistent with repository signals (2 stars, 0 forks, 1 contributor, not listed on registry). This strongly suggests fabricated/manipulated install metrics designed to inflate trust and bypass security heuristics that give benefit of the doubt to popular packages. (location: metadata.json)
scope violation
skill_description in metadata.json is 'width=device-width, initial-scale=1' — an HTML meta viewport tag, not a legitimate skill description. This indicates the metadata was not properly authored and the skill has no actual defined functionality (SKILL.md is empty), suggesting either a placeholder for future malicious content or a broken/fraudulent listing. (location: metadata.json, SKILL.md)
typosquat
Skill named 'svelte5-best-practices' in a generic 'agent-skills' repo with fabricated popularity metrics. The name targets a popular search term (Svelte 5) to attract developers, while the repo has no actual skill content. Combined with inflated install count, this pattern resembles a name-squatting or typosquatting setup to impersonate legitimate Svelte ecosystem tooling. (location: metadata.json)
curl https://api.brin.sh/skill/ejirocodes%2Fagent-skills%2Fsvelte5-best-practicesCommon questions teams ask before deciding whether to use this skill in agent workflows.
ejirocodes/agent-skills/svelte5-best-practices currently scores 38/100 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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