Trust Assessment
huckleberry 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 Git dependency in installation instructions.
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 14, 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 Git dependency in installation instructions The skill's setup instructions direct users to install `py-huckleberry-api` directly from a GitHub repository using `pip install git+...`. This practice is a significant supply chain risk as the content of the remote repository's default branch can change at any time without versioning, potentially introducing vulnerabilities or malicious code into the user's environment without their explicit knowledge or consent. Pin the dependency to a specific commit hash (e.g., `pip install git+https://github.com/Woyken/py-huckleberry-api.git@<commit_hash>`) or, preferably, wait for a stable release to be published on PyPI and install from there. If installing from Git is unavoidable, advise users to audit the specific commit they are installing. | LLM | SKILL.md:13 |
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