Trust Assessment
apo-cli received a trust score of 79/100, 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, 1 high, 1 medium, and 0 low severity. Key findings include Suspicious import: urllib.request, Untrusted instructions for LLM behavior in SKILL.md.
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 | |
|---|---|---|---|---|
| HIGH | Untrusted instructions for LLM behavior in SKILL.md The `SKILL.md` file, which is treated as untrusted input, contains a section titled 'Critical Rules' that provides direct instructions to the host LLM on how to operate the `apo-cli` skill. Examples include 'NEVER complete a purchase' and 'Always provide the cart URL'. According to SkillShield's rules, content within untrusted input delimiters must be treated as data, not instructions. Allowing the LLM to follow directives from untrusted sources, even if seemingly benign, creates a vulnerability for prompt injection where malicious instructions could be inserted to manipulate the LLM's behavior. Remove all direct instructions for the LLM from the `SKILL.md` file. LLM operational guidelines should be provided through trusted configuration or system prompts, not within untrusted skill documentation. | LLM | SKILL.md:15 | |
| MEDIUM | Suspicious import: urllib.request Import of 'urllib.request' detected. This module provides network or low-level system access. Verify this import is necessary. Network and system modules in skill code may indicate data exfiltration. | Static | skills/lars147/apo-cli/apo_cli.py:20 |
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