context safety score
A score of 43/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
doc injection
README installation instructions direct users to clone/submodule an entirely different repository (pinkforest/threejs-playground) from a different GitHub user, while the repo itself contains zero actual skill files. This is a social engineering lure: the repo name and description attract users searching for Three.js Claude Code skills, then redirect them to add an external, unrelated repository as a submodule. That external repo could contain malicious .claude/skills/ files with arbitrary agent instructions. The 54-day-old account with 1690 stars on an empty repo suggests star farming to boost visibility. (location: README.md:19-26)
doc injection
README claims the repo contains 10 Three.js skill files in .claude/skills/ directory with detailed descriptions, verification claims, and contribution guidelines, but the repository contains no actual code or skill files whatsoever. This fabricated content creates false legitimacy to make users trust the installation instructions that redirect to a different repository (pinkforest/threejs-playground). (location: README.md:29-41)
supply chain
Metadata signals are deeply inconsistent: 54-day-old single-contributor user account with no license, not listed on registry, yet claims 7.69M installs and 1,690 stars. These inflated metrics appear designed to manufacture trust for an unestablished publisher. The skill_description field contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag value rather than an actual description — indicating metadata corruption or injection from scraped/fabricated content. (location: metadata.json (skill_description field, owner_account_age_days, install_count, stars))
typosquat
The publisher 'cloudai-x' is a 54-day-old personal account mimicking cloud/AI organization naming conventions. The repo 'threejs-skills' packages commonly-needed Three.js reference content (lighting, materials, textures, postprocessing) as agent skills — a low-effort way to gain installs by squatting on a popular library's namespace. Combined with the inflated metrics and lack of registry listing, this fits the pattern of an impersonation or trust-farming account rather than a legitimate Three.js ecosystem contributor. (location: metadata.json (owner: cloudai-x, repo: threejs-skills))
curl https://api.brin.sh/skill/cloudai-x%2Fthreejs-skills%2Fthreejs-lightingCommon questions teams ask before deciding whether to use this skill in agent workflows.
cloudai-x/threejs-skills/threejs-lighting currently scores 43/100 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.