Security Audit

woodpecker-co-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

woodpecker-co-automation received a trust score of 76/100, 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, 2 high, 0 medium, and 0 low severity. Key findings include Unpinned Rube MCP dependency, `RUBE_REMOTE_WORKBENCH` suggests arbitrary code 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 20, 2026 (commit 27904475). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

100%

Static Code Analysis

100%

Dependency Graph

100%

LLM Behavioral Safety

70%

Behavioral Risk Signals

Shell Execution

2 findings

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Unpinned Rube MCP dependency The skill manifest specifies a dependency on the 'rube' MCP without a version constraint. This allows any future version of 'rube' to be used, including potentially malicious or incompatible versions, introducing a significant supply chain risk. A compromised or malicious update to the 'rube' MCP could lead to arbitrary code execution or data exfiltration. Pin the 'rube' MCP dependency to a specific, known-good version (e.g., `{"mcp": ["rube@1.2.3"]}`) to ensure stability and security. Regularly review and update the pinned version.	LLM	SKILL.md
HIGH	`RUBE_REMOTE_WORKBENCH` suggests arbitrary code execution The `RUBE_REMOTE_WORKBENCH` tool, particularly when combined with `run_composio_tool()`, strongly suggests the capability to execute arbitrary code or perform highly privileged operations within a remote environment. Without clear documentation on its sandboxing, input validation, and allowed operations, this presents a significant command injection and excessive permissions risk. An attacker could potentially craft inputs to execute malicious commands or access sensitive resources on the remote workbench. Provide clear documentation on the security model, sandboxing, and input validation for `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Restrict its capabilities or ensure strict input sanitization to prevent arbitrary code execution. If not strictly necessary, consider removing or limiting access to this tool.	LLM	SKILL.md:80

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