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
description injection
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML meta viewport tag fragment injected as the skill description. This is not a legitimate capability description; it is either a scraping artifact indicating the metadata was fabricated/auto-generated from HTML, or an attempt to inject unexpected content into agent context. Combined with an empty SKILL.md, this skill has no legitimate documented purpose. (location: metadata.json:skill_description)
scope violation
SKILL.md is completely empty (0 bytes) while the repo is named 'skills-template' — the skill declares no capabilities, no tools, no parameters, and no purpose whatsoever. A skill with zero documented functionality and zero content is inherently deceptive if distributed as a usable skill, as agents cannot make informed decisions about what it does. (location: SKILL.md)
supply chain
Extreme mismatch between trust signals: 13 stars, 2 forks, 3 contributors, no license, not listed on registry, org not verified — yet claims 7,690,000 installs. This install count is implausible for a repo with 13 stars and suggests fabricated or inflated metrics. The repo name 'skills-template' combined with an empty SKILL.md and an HTML-fragment description indicates this is not a legitimate production skill. (location: metadata.json)
curl https://api.brin.sh/skill/supercent-io%2Fskills-template%2Fprompt-repetitionCommon questions teams ask before deciding whether to use this skill in agent workflows.
supercent-io/skills-template/prompt-repetition 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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