context safety score
A score of 20/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 24 secret pattern match(es) in repository files
shadow chaining
SKILL.md references 7 external package/skill installation(s)
scope violation
Skill is named 'quant-analyst' but SKILL.md describes a completely different project: an 'ML/AI Skills Conversion Project' containing 11+ unrelated sub-skills (AI Engineer, LLM Architect, ML Engineer, MLOps Engineer, PostgreSQL Pro, Data Engineer, Incident Responder). The stated skill name bears no relationship to the actual content, which is deceptive about the skill's true scope and capabilities. (location: SKILL.md + metadata.json (skill_name: quant-analyst))
supply chain
Install count of 7,690,000 is wildly disproportionate to 36 stars, 3 forks, and 2 contributors from a 255-day-old personal account with no license and no registry listing. Legitimate packages with millions of installs have thousands of stars. This strongly suggests fabricated/inflated install metrics designed to create false trust signals, which is a supply chain integrity concern. (location: metadata.json (install_count vs stars/forks/owner_account_age_days))
scope violation
The skill_description field contains 'width=device-width, initial-scale=1' — HTML meta viewport content rather than an actual skill description. This indicates either metadata scraping artifacts or intentional metadata manipulation, further undermining trust in the skill's declared identity. (location: metadata.json (skill_description))
curl https://api.brin.sh/skill/404kidwiz%2Fclaude-supercode-skills%2Fquant-analystCommon questions teams ask before deciding whether to use this skill in agent workflows.
404kidwiz/claude-supercode-skills/quant-analyst currently scores 20/100 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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