context safety score
A score of 49/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 16 secret pattern match(es) in repository files
supply chain
Found 2 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 2 remote script pattern(s) in documentation (likely install instructions, not executable)
typosquat
Skill claims to be 'k8s-manifest-generator' from repo 'wshobson/agents' but SKILL.md is completely empty (0 lines) and skill_description is 'width=device-width, initial-scale=1' — an HTML meta viewport attribute, not a real description. This indicates metadata was scraped/fabricated from a popular project's webpage. The generic repo name 'agents' combined with a personal account (not org-verified), no registry listing despite claiming 7.69M installs and 29K stars, strongly suggests impersonation of a well-known agents framework. (location: metadata.json:skill_description, SKILL.md)
scope violation
The skill presents itself as 'k8s-manifest-generator' but provides absolutely zero skill definition content — SKILL.md is empty. There is no tool description, no parameters, no documented capabilities whatsoever. A skill with no content but fabricated trust signals (spoofed star count, fake install metrics) is designed to gain installation trust while providing no legitimate functionality, potentially serving as a placeholder for future malicious content injection. (location: SKILL.md (empty file))
curl https://api.brin.sh/skill/wshobson%2Fagents%2Fk8s-manifest-generatorCommon questions teams ask before deciding whether to use this skill in agent workflows.
wshobson/agents/k8s-manifest-generator currently scores 49/100 with a suspicious verdict and medium 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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