context safety score
A score of 46/100 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
shadow chaining
SKILL.md references 2 external package/skill installation(s)
supply chain
SKILL.md instructs cloning code from github.com/webzler/agentMemory — a completely different GitHub account/repo than the listed source sickn33/antigravity-awesome-skills. Users evaluate trust signals for one repository but execute code from another, enabling supply chain redirection. (location: SKILL.md:23)
supply chain
metadata.json skill_description field contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag value, not a real description. This indicates metadata was scraped from a webpage or fabricated, undermining the trustworthiness of all reported metrics (stars, installs, contributors). (location: metadata.json:1 (skill_description field))
typosquat
Repository 'antigravity-awesome-skills' uses the well-known 'awesome-*' naming convention popular on GitHub for curated lists, combined with a personal account only 510 days old, no registry listing, and no org verification — despite claiming 17K stars and 7.69M installs. The mismatch between claimed popularity and lack of registry presence is suspicious. (location: metadata.json:1)
curl https://api.brin.sh/skill/sickn33%2Fantigravity-awesome-skills%2Fagent-memory-mcpCommon questions teams ask before deciding whether to use this skill in agent workflows.
sickn33/antigravity-awesome-skills/agent-memory-mcp currently scores 46/100 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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