Trust Assessment
context-optimizer 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 Missing required field: name, Unpinned dependencies in skill description.
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 | Missing required field: name The 'name' field is required for claude_code skills but is missing from frontmatter. Add a 'name' field to the SKILL.md frontmatter. | Static | skills/smartpeopleconnected/token-optimizer/skills/context-optimizer/SKILL.md:1 | |
| MEDIUM | Unpinned dependencies in skill description The `SKILL.md` file lists `tiktoken` and `@xenova/transformers` as dependencies without specifying version ranges. This practice can lead to supply chain vulnerabilities if a malicious update to one of these packages is released, as the skill would automatically pull in the latest (potentially compromised) version. While the `package.json` is not provided, the description in `SKILL.md` indicates this risk. Pin dependency versions in the `package.json` file (e.g., `tiktoken: "^0.4.0"`, `@xenova/transformers: "^2.17.0"`). This ensures that only explicitly approved versions or versions within a safe range are used. | LLM | SKILL.md:39 |
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