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)
scope violation
SKILL.md is completely empty — the skill provides zero documentation of its purpose, capabilities, or parameters. Combined with a repo named 'skills-template' (suggesting an official template) but shipping an actual skill named 'ralph', this conceals what the skill actually does. Users and agents cannot make an informed trust decision about a skill with no description. (location: SKILL.md)
description injection
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag value, not a legitimate skill description. This is either content injection from HTML scraping or a deliberately nonsensical description that could cause unexpected agent behavior when the agent tries to interpret the skill's purpose. (location: metadata.json:skill_description)
supply chain
Extreme mismatch between claimed install count (7,690,000) and actual popularity signals (13 stars, 2 forks, not listed on registry, org not verified). A skill with nearly 8 million installs would have significant community engagement. This pattern suggests fabricated install metrics, which could be used to bypass trust heuristics that give benefit-of-the-doubt to popular packages. (location: metadata.json)
curl https://api.brin.sh/skill/supercent-io%2Fskills-template%2FralphCommon questions teams ask before deciding whether to use this skill in agent workflows.
supercent-io/skills-template/ralph 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.