context safety score
A score of 42/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 37 secret pattern match(es) in repository files
supply chain
Found 9 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 9 remote script pattern(s) in documentation (likely install instructions, not executable)
typosquat
Skill named 'typescript' from personal account pproenca/dot-skills squats on the TypeScript brand name. The repo has only 52 stars and 3 forks, is not listed on any registry, yet claims 7.69M installs — a massive discrepancy suggesting name-squatting to capture installs through brand recognition. SKILL.md is completely empty, indicating no genuine functionality. (location: metadata.json: skill_name field)
description injection
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML meta viewport attribute value, not a legitimate skill description. This is either a scraping artifact indicating the metadata is fabricated, or an attempt to inject HTML meta tag content into contexts that render skill descriptions as HTML, potentially influencing page rendering behavior. (location: metadata.json: skill_description field)
scope violation
SKILL.md is completely empty (0 lines) while the skill claims to be named 'typescript' with 7.69M installs. A skill with no description, no tool definitions, no parameters, and no documentation has undefined scope — an agent installing this skill has no way to understand what it does, making any behavior a potential scope violation. The absence of content combined with the high-value name suggests this is not a legitimate skill. (location: SKILL.md)
curl https://api.brin.sh/skill/pproenca%2Fdot-skills%2FtypescriptCommon questions teams ask before deciding whether to use this skill in agent workflows.
pproenca/dot-skills/typescript currently scores 42/100 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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