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 3 secret pattern match(es) in repository files
typosquat
Owner 'anthropics' is a near-identical misspelling of 'anthropic' (the real Anthropic company's GitHub org). Combined with the repo name 'claude-plugins-official' using 'official' branding and the 'claude' product name, this is a strong impersonation/typosquatting pattern. The real Anthropic org on GitHub is 'anthropic' without a trailing 's'. Despite high install counts and stars (which can be faked or bought), the repo has no license and is not listed on the skill registry — inconsistent with a genuine official release. (location: metadata.json: owner='anthropics', repo='claude-plugins-official')
description injection
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag value rather than a legitimate skill description. This could be an injection targeting HTML rendering contexts or simply corrupted metadata, but it is abnormal and not a valid description. (location: metadata.json: skill_description)
scope violation
SKILL.md is completely empty (0 bytes). A skill with 7.69M claimed installs and no documentation whatsoever provides zero transparency about what capabilities it registers or what it does. Combined with the typosquatting signals, an empty SKILL.md means there is no way to verify the skill's stated purpose against its actual behavior. (location: SKILL.md)
curl https://api.brin.sh/skill/anthropics%2Fclaude-plugins-official%2Fclaude-automation-recommenderCommon questions teams ask before deciding whether to use this skill in agent workflows.
anthropics/claude-plugins-official/claude-automation-recommender 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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