context safety score
A score of 31/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 14 secret pattern match(es) in repository files
supply chain
Found 8 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 8 remote script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 5 unexpected binary file(s) in source repository
doc injection
AGENTS.md falsely claims authorship by 'Vercel Engineering' (lines 3-4) and states it is for agents/LLMs working 'at Vercel' (lines 7-11). The repository is owned by supercent-io, an unverified organization with 13 stars, not by Vercel. This false attribution gives the agent configuration file unearned authority when consumed by AI agents, who would treat the instructions as coming from Vercel's official engineering team. The technical content itself is legitimate React best practices with no malicious instructions. (location: agent-configs/.agent-skills__react-best-practices__AGENTS.md:3-11)
typosquat
Skill claims name 'debugging' (a highly generic, desirable namespace) from repo 'skills-template' with extreme install count inflation: 7.69M installs but only 13 stars. Unverified org, not listed on registry, empty SKILL.md, and skill_description is a scraped HTML meta viewport value ('width=device-width, initial-scale=1') rather than actual content. This pattern is consistent with namespace squatting on a high-value skill name with artificially inflated trust signals. (location: metadata.json (skill_name, install_count, skill_description))
scope violation
SKILL.md is completely empty (0 lines) meaning this skill has no documented capabilities whatsoever, yet claims 7.69M installs. An agent installing this skill has zero information about what it actually does. The skill_description field contains 'width=device-width, initial-scale=1' (an HTML meta viewport value), indicating the description was not authored but scraped incorrectly from a webpage, suggesting the skill metadata is fabricated or auto-generated rather than representing a real tool. (location: SKILL.md (empty), metadata.json (skill_description))
curl https://api.brin.sh/skill/supercent-io%2Fskills-template%2FdebuggingCommon questions teams ask before deciding whether to use this skill in agent workflows.
supercent-io/skills-template/debugging currently scores 31/100 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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