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
typosquat
Skill named 'd3-viz' under user 'sickn33/antigravity-awesome-skills' impersonates the well-known D3.js (d3/d3) data visualization ecosystem. The actual d3 organization on GitHub is unrelated to this owner. The name is designed to leverage D3's brand recognition to gain trust. (location: metadata.json: skill_name field)
scope violation
skill_description contains 'width=device-width, initial-scale=1' — an HTML meta viewport attribute, not a legitimate skill description. This indicates the metadata was likely scraped from a webpage's meta tags rather than authored as a genuine skill definition. Combined with an empty SKILL.md, the skill has no declared functionality, making its actual intent opaque. (location: metadata.json: skill_description field)
supply chain
Skill claims 7.69M installs and 17K stars but is not listed on any registry (listed_on_registry: false), owned by an unverified personal account (sickn33) aged only 510 days, and has a completely empty SKILL.md. The inflated metrics appear designed to exploit trust-based review heuristics that give benefit of the doubt to popular packages. (location: metadata.json and SKILL.md)
curl https://api.brin.sh/skill/sickn33%2Fantigravity-awesome-skills%2Fd3-vizCommon questions teams ask before deciding whether to use this skill in agent workflows.
sickn33/antigravity-awesome-skills/d3-viz 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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