context safety score
A score of 39/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 34 secret pattern match(es) in repository files
supply chain
Found 7 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 7 remote script pattern(s) in documentation (likely install instructions, not executable)
typosquat
Repository named 'claude-plugin-marketplace' impersonates an official Anthropic/Claude plugin ecosystem. The name strongly implies an official or sanctioned marketplace for Claude plugins, which this is not. Single contributor, 18 stars, not org-verified, not listed on registry. (location: metadata.json: full_name 'josiahsiegel/claude-plugin-marketplace')
scope violation
Skill is named 'docker-best-practices' but lives in a repo called 'claude-plugin-marketplace'. The skill_description field contains 'width=device-width, initial-scale=1' — an HTML meta viewport attribute, not a legitimate description. SKILL.md is completely empty (0 bytes). The skill has no actual content defining what it does, meaning an agent installing this gets an undefined/empty capability under the guise of 'docker-best-practices'. (location: SKILL.md (empty), metadata.json: skill_description)
supply chain
Install count of 7,690,000 is wildly inconsistent with 18 stars and 1 contributor. This inflated metric could trick trust-based evaluation systems into granting higher confidence to a skill that has no meaningful content or community validation. Not listed on any registry despite claimed massive adoption. (location: metadata.json: install_count vs stars/contributors)
curl https://api.brin.sh/skill/josiahsiegel%2Fclaude-plugin-marketplace%2Fdocker-best-practicesCommon questions teams ask before deciding whether to use this skill in agent workflows.
josiahsiegel/claude-plugin-marketplace/docker-best-practices currently scores 39/100 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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