Security Audit
beaconchain-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
beaconchain-automation received a trust score of 85/100, 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, 0 high, 1 medium, and 0 low severity. Key findings include Excessive Tool Access via RUBE_REMOTE_WORKBENCH.
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 20, 2026 (commit 27904475). 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 | Excessive Tool Access via RUBE_REMOTE_WORKBENCH The skill instructs the LLM to use `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()`. This suggests the ability to execute any tool within the broader Composio ecosystem, not just those specific to Beaconchain. If the Composio ecosystem contains tools with broad system access (e.g., file system, network, arbitrary code execution), this instruction grants the LLM a very wide scope of action, potentially exceeding the principle of least privilege for 'Beaconchain automation'. This could allow the LLM to perform actions beyond its intended scope if not properly constrained by the host environment. If `RUBE_REMOTE_WORKBENCH` allows execution of arbitrary Composio tools, consider restricting the LLM's access to this tool or ensuring that the `run_composio_tool()` function is strictly scoped to Beaconchain-related operations. Alternatively, provide more specific tool calls rather than a generic workbench for bulk operations if the intent is limited. | LLM | SKILL.md:70 |
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