context safety score
A score of 44/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 37 secret pattern match(es) in repository files
supply chain
Found 9 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 9 remote script pattern(s) in documentation (likely install instructions, not executable)
typosquat
Skill named 'nuqs' published by pproenca/dot-skills impersonates the well-known npm package 'nuqs' (by 47ng), a popular type-safe search params state manager for React/Next.js with 4k+ GitHub stars. The real nuqs has no relation to this publisher. The skill_description field contains 'width=device-width, initial-scale=1' (an HTML viewport meta tag), suggesting the description was scraped from HTML rather than authored legitimately. Combined with an empty SKILL.md and not being listed on the registry, this appears to be a name-squatting attempt on a popular package name. (location: metadata.json (skill_name field), SKILL.md (empty))
scope violation
SKILL.md is completely empty (0 lines), meaning the skill provides zero documentation about what it actually does. For a skill claiming 7.69M installs, having no skill description or documentation is a red flag — there is no way for an agent to understand what capabilities are being granted. The skill_description in metadata.json is nonsensical HTML metadata ('width=device-width, initial-scale=1'), not a functional description. (location: SKILL.md, metadata.json (skill_description field))
curl https://api.brin.sh/skill/pproenca%2Fdot-skills%2FnuqsCommon questions teams ask before deciding whether to use this skill in agent workflows.
pproenca/dot-skills/nuqs currently scores 44/100 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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