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 84 secret pattern match(es) in repository files
supply chain
Found 12 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 12 remote script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 4 unexpected binary file(s) in source repository
description injection
skill_description in metadata.json contains 'width=device-width, initial-scale=1' — an HTML meta viewport attribute, not a legitimate skill description. This is injected content in the description field that could confuse agent parsers or exploit rendering contexts. (location: metadata.json:skill_description)
scope violation
SKILL.md is completely empty (0 bytes) while claiming 17k stars and 7.6M installs. A skill with no defined capabilities, no tool definitions, and no documentation is fundamentally deceptive — it presents itself as a legitimate popular skill while providing no actual skill definition. Combined with the HTML-fragment description, this appears to be a placeholder designed to exploit trust signals rather than provide real functionality. (location: SKILL.md)
typosquat
Repository named 'antigravity-awesome-skills' mimics the well-known 'awesome-*' naming convention (awesome-python, awesome-go, etc.) to borrow credibility. The skill name 'backend-patterns' is generically authoritative. Owner 'sickn33' is a 510-day-old unverified personal account, not listed on registry, yet claims 7.6M installs — the trust signals are inconsistent with the empty content, suggesting artificially inflated metrics or a deceptive listing. (location: metadata.json:full_name, skill_name)
curl https://api.brin.sh/skill/sickn33%2Fantigravity-awesome-skills%2Fbackend-patternsCommon questions teams ask before deciding whether to use this skill in agent workflows.
sickn33/antigravity-awesome-skills/backend-patterns 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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