Trust Assessment
bear-notes received a trust score of 86/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 1 finding: 0 critical, 1 high, 0 medium, and 0 low severity. Key findings include Unpinned dependency in installation.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. All layers scored 70 or above, reflecting consistent security practices.
Last analyzed on February 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Unpinned dependency in installation The `grizzly` tool is installed using `go install github.com/tylerwince/grizzly/cmd/grizzly@latest`. Using `@latest` means the exact version is not pinned, which can lead to supply chain vulnerabilities if a malicious update is pushed to the `grizzly` repository. An attacker could introduce malicious code into a new 'latest' version, which would then be installed by users of this skill without explicit review. Pin the dependency to a specific version or commit hash (e.g., `github.com/tylerwince/grizzly/cmd/grizzly@v1.2.3` or `@<commit_hash>`) to ensure deterministic and secure installations. | LLM | SKILL.md |
Scan History
Embed Code
[](https://skillshield.io/report/22823f7c7c3061cb)
Powered by SkillShield