context safety score
A score of 27/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 10 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 4 remote script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Install count (7.69M) is wildly disproportionate to repository signals (6 stars, 0 forks, 132-day-old unverified org, not listed on registry, no license). This is a strong indicator of fabricated/manipulated install metrics designed to inflate trust and bypass vetting. (location: metadata.json)
shadow chaining
SKILL.md Section 'Integration with Other Skills' explicitly instructs the agent to invoke another skill ('content-creator') after analysis. This chains skill execution without user initiation, potentially enabling an attack chain if the referenced skill is malicious. (location: SKILL.md:246-250)
scope violation
The skill_description field in metadata contains 'width=device-width, initial-scale=1' — an HTML meta viewport tag value, not a legitimate skill description. This is a metadata injection artifact indicating the metadata was either manipulated or scraped from HTML in a way that corrupts the skill's identity information. (location: metadata.json (skill_description field))
scope violation
SKILL.md instructs the agent to execute a Python script at 'library/agents/.claude/skills/youtube-video-analyst/scripts/fetch_transcript.py' but no such script exists in the repository. This references an external/non-distributed executable, which could be supplied later via a different vector. (location: SKILL.md:34)
curl https://api.brin.sh/skill/shipshitdev%2Flibrary%2Fyoutube-video-analystCommon questions teams ask before deciding whether to use this skill in agent workflows.
shipshitdev/library/youtube-video-analyst currently scores 27/100 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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