context safety score
A score of 42/100 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 is named 'zod' — the exact name of the hugely popular TypeScript schema validation library (colinhacks/zod, 35k+ GitHub stars). Published by unrelated personal account 'pproenca/dot-skills' with only 52 stars, not listed on the skills registry, empty SKILL.md, and a nonsensical skill_description ('width=device-width, initial-scale=1' — an HTML meta tag fragment). This has all hallmarks of name-squatting on a well-known project name. (location: metadata.json (skill_name: 'zod', full_name: 'pproenca/dot-skills'))
scope violation
SKILL.md is completely empty — the skill defines zero documented capabilities, tools, or parameters. An agent skill with no description provides no way to verify what it actually does, making it impossible to assess whether runtime behavior matches stated purpose. Combined with the typosquat name, this suggests the skill is a shell that could serve as a vector for injecting arbitrary capability at install time. (location: SKILL.md (empty file, 0 lines))
curl https://api.brin.sh/skill/pproenca%2Fdot-skills%2FzodCommon questions teams ask before deciding whether to use this skill in agent workflows.
pproenca/dot-skills/zod currently scores 42/100 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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