Trust Assessment
agentic-ai-gold received a trust score of 87/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 2 findings: 0 critical, 0 high, 2 medium, and 0 low severity. Key findings include Sensitive environment variable access: $HOME, Unpinned Python package dependencies in install script.
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
Behavioral Risk Signals
Security Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Sensitive environment variable access: $HOME Access to sensitive environment variable '$HOME' detected in shell context. Verify this environment variable access is necessary and the value is not exfiltrated. | Static | skills/bsouto319/brunosouto1108/install.sh:29 | |
| MEDIUM | Unpinned Python package dependencies in install script The `install.sh` script installs several Python packages (`langgraph`, `openai-agents`, `crewai`, `pydantic-ai`, `mem0`, `zep-python`) without specifying exact versions. This practice can lead to supply chain vulnerabilities, as future versions of these packages might introduce breaking changes, security flaws, or malicious code. Without pinned versions, the skill's behavior and security posture are not reproducible or guaranteed over time. Pin all Python package dependencies to exact versions (e.g., `package==1.2.3`) in the `install.sh` script. Alternatively, use a `requirements.txt` file with pinned versions and install it using `pip install -r requirements.txt`. | LLM | install.sh:18 |
Scan History
Embed Code
[](https://skillshield.io/report/89823a215c3709d0)
Powered by SkillShield