Security Audit
coassemble-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
coassemble-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 2 findings: 0 critical, 0 high, 0 medium, and 0 low severity. Key findings include Broad Tool Access to Coassemble Operations, Broad Tool Access to Coassemble Operations via 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 Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| INFO | Broad Tool Access to Coassemble Operations The skill provides access to a wide range of Coassemble operations through `RUBE_MULTI_EXECUTE_TOOL`. While this is inherent to an 'automation' skill, it means an agent using this skill could potentially perform any action available via the Coassemble toolkit. This requires careful consideration of the agent's permissions and scope to prevent unintended or malicious actions within Coassemble. Ensure that the LLM agent using this skill operates within a carefully defined scope and has appropriate guardrails. Implement monitoring for actions performed via this skill. Review the specific Coassemble toolkit permissions to ensure they are not broader than necessary for the intended use cases. | LLM | SKILL.md:64 | |
| INFO | Broad Tool Access to Coassemble Operations via Remote Workbench The skill explicitly mentions `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops', indicating the ability to execute arbitrary Composio tools related to Coassemble. This grants broad programmatic control over Coassemble, which, while intended for automation, necessitates strict control over the agent's operational context. Ensure that the LLM agent using this skill operates within a carefully defined scope and has appropriate guardrails. Implement monitoring for actions performed via this skill. Review the specific Coassemble toolkit permissions to ensure they are not broader than necessary for the intended use cases. | LLM | SKILL.md:94 |
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