context safety score
A score of 43/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 41 secret pattern match(es) in repository files
supply chain
Found 3 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 1 remote script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 2 unexpected binary file(s) in source repository
typosquat
Skill named 'tailwind-css-patterns' in a generic 'developer-kit' repo from an unverified individual account (441 days old, 116 stars). The name leverages the well-known Tailwind CSS brand. The massive claimed install count (7.69M) is wildly disproportionate to 116 stars and 3 contributors, and the skill is not listed on any registry, suggesting inflated or fabricated metrics to build false trust. (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. This indicates the skill metadata was either scraped incorrectly from HTML or deliberately set to a nonsensical value. Combined with an empty SKILL.md (zero content), this skill provides no documented functionality whatsoever, making its actual behavior completely opaque. (location: metadata.json:skill_description, SKILL.md)
curl https://api.brin.sh/skill/giuseppe-trisciuoglio%2Fdeveloper-kit%2Ftailwind-css-patternsCommon questions teams ask before deciding whether to use this skill in agent workflows.
giuseppe-trisciuoglio/developer-kit/tailwind-css-patterns currently scores 43/100 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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