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
typosquat
Skill claims the generic high-value name 'task-planning' but has an empty SKILL.md, a nonsensical skill_description that is actually an HTML viewport meta tag ('width=device-width, initial-scale=1'), only 13 stars despite claiming 7.69M installs, is not listed on the registry, and is not org-verified. The extreme mismatch between install count and stars (13 stars / 7.69M installs) suggests fabricated metrics. This appears to be namespace squatting on a common skill concept to intercept agents searching for task-planning capabilities. (location: metadata.json (skill_name, skill_description, install_count vs stars))
scope violation
The skill_description field contains 'width=device-width, initial-scale=1' which is an HTML meta viewport tag, not a description of the skill's capabilities. This means agents cannot make an informed decision about what this skill actually does before invoking it, since the description is nonsensical. Combined with an empty SKILL.md, the skill provides zero legitimate documentation of its actual behavior. (location: metadata.json (skill_description) and SKILL.md (empty))
curl https://api.brin.sh/skill/supercent-io%2Fskills-template%2Ftask-planningCommon questions teams ask before deciding whether to use this skill in agent workflows.
supercent-io/skills-template/task-planning 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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