Security Audit
opengraph-io-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
opengraph-io-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 Skill documentation promotes use of generic tool execution with broad scope.
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 | Skill documentation promotes use of generic tool execution with broad scope The skill's documentation, specifically in the 'Quick Reference' section, mentions `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' using `run_composio_tool()`. This tool allows for the execution of arbitrary Composio tools, which grants a very broad scope of operations beyond the stated purpose of 'Opengraph IO Automation'. An AI agent using this skill might be prompted to use `RUBE_REMOTE_WORKBENCH` to execute tools unrelated to Opengraph IO, potentially leading to unintended actions or access to other systems if not properly constrained by the agent's internal logic or the Rube MCP system. Consider restricting the scope of tools available via `RUBE_REMOTE_WORKBENCH` when used in the context of this specific skill, or clarify that its use should be strictly limited to Opengraph IO-related operations. Alternatively, remove `RUBE_REMOTE_WORKBENCH` from the skill's documentation if it's not strictly necessary for Opengraph IO automation. | LLM | SKILL.md:69 |
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