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
The skill_description field in metadata.json contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag value, not a legitimate skill description. This is injected metadata that could exploit downstream systems that render or process skill descriptions without sanitization (e.g., XSS in web dashboards, or confusing agent UIs that display skill metadata). (location: metadata.json:skill_description field)
credential exposure
SKILL.md instructs the agent to write OPENAI_API_KEY and XAI_API_KEY into a dotfile at ~/.config/last30days/.env, then passes control to an external Python script (last30days.py) not included in this repository. The script at ~/.claude/skills/last30days/scripts/ could read these credentials at runtime. Since the script source is not present for inspection, the credential handling cannot be verified. (location: SKILL.md:67-83 and SKILL.md:96)
curl https://api.brin.sh/skill/sickn33%2Fantigravity-awesome-skills%2Flast30daysCommon questions teams ask before deciding whether to use this skill in agent workflows.
sickn33/antigravity-awesome-skills/last30days 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.