Security Audit
productlane-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
productlane-automation received a trust score of 94/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Broad access to Productlane operations via dynamic tool execution.
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 17, 2026 (commit 99e2a295). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Broad access to Productlane operations via dynamic tool execution The skill's design allows the LLM to dynamically discover and execute any operation exposed by the 'productlane' toolkit through Rube MCP. By leveraging `RUBE_SEARCH_TOOLS` and `RUBE_MULTI_EXECUTE_TOOL`, the LLM gains broad, unconstrained access to all functionalities of the connected Productlane account, rather than being limited to specific, predefined actions. This increases the potential blast radius if the LLM is compromised or misinterprets instructions, as it can perform any action the Productlane toolkit permits. Consider if the skill's scope can be narrowed to specific Productlane operations rather than allowing dynamic execution of any discovered tool. If full dynamic access is intended, ensure robust authorization, auditing, and monitoring are in place for the Rube MCP and Productlane integration. Clearly communicate the broad access implications to users. | LLM | SKILL.md:48 |
Scan History
Embed Code
[](https://skillshield.io/report/ad5793b819a84e9a)
Powered by SkillShield