context safety score
A score of 49/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 16 secret pattern match(es) in repository files
supply chain
Found 2 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 2 remote script pattern(s) in documentation (likely install instructions, not executable)
typosquat
Repository 'wshobson/agents' uses the highly generic name 'agents' under a personal account, occupying a namespace that could be confused with well-known agent frameworks. The skill name 'python-observability' claims a broad, desirable namespace. Combined with the empty SKILL.md and fabricated metadata (skill_description is an HTML viewport meta tag 'width=device-width, initial-scale=1' rather than an actual description), this appears to be a squatted namespace rather than a legitimate skill. (location: metadata.json: skill_name, full_name)
scope violation
SKILL.md is completely empty (0 lines), meaning this skill provides zero documentation of its actual capabilities. An agent installing this skill has no way to understand what it does. Despite claiming 7.69M installs and 29K stars, the skill has no definition — the core contract between skill and agent is absent. The metadata skill_description field contains 'width=device-width, initial-scale=1' (an HTML viewport meta tag), suggesting the metadata was scraped from a webpage rather than authored as a legitimate skill descriptor. (location: SKILL.md (empty), metadata.json: skill_description)
curl https://api.brin.sh/skill/wshobson%2Fagents%2Fpython-observabilityCommon questions teams ask before deciding whether to use this skill in agent workflows.
wshobson/agents/python-observability currently scores 49/100 with a suspicious verdict and medium 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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