context safety score
A score of 28/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
github api error
Could not fetch GitHub metadata: GitHub API returned 404: {"message":"Not Found","documentation_url":"https://docs.github.com/rest/repos/repos#get-a-repository","status":"404"}
typosquat
Skill named 'supabase-best-practices' uses the well-known Supabase brand but has no connection to Supabase: empty owner, empty repo URL, org_verified=false, 0 stars, 0 contributors. Empty SKILL.md and no actual content suggest this is brand-squatting to deceive agents or users into trusting it based on the Supabase name alone. (location: metadata.json (skill_name, owner, full_name, org_verified))
scope violation
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML meta viewport attribute, not a legitimate skill description. This is either a web scraping artifact indicating the skill metadata was fabricated/scraped, or a deceptive placeholder. Combined with an entirely empty SKILL.md, this skill has no documented purpose yet claims 7.69M installs. The install count is wildly inconsistent with 0 stars/forks/contributors, suggesting count manipulation. (location: metadata.json (skill_description, install_count))
supply chain
All repository identity fields are empty (owner, repo, full_name) and owner_account_age_days is null. This skill cannot be traced back to any source repository or author. Combined with not being listed on the registry (listed_on_registry=false), there is no supply chain provenance — users cannot verify what code they would be installing. (location: metadata.json (owner, repo, full_name, owner_account_age_days, listed_on_registry))
curl https://api.brin.sh/skill/pedrobarretocw%2Fsupabase-best-practices%2Fsupabase-best-practicesCommon questions teams ask before deciding whether to use this skill in agent workflows.
pedrobarretocw/supabase-best-practices/supabase-best-practices currently scores 28/100 with a suspicious verdict and low 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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