Security Audit

abhinag007/openclaw-skill:skills/agentlink

github.com/abhinag007/openclaw-skill

AI SkillCommit 465e2bf55ccd

TRUSTED

Scanned about 1 month ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

abhinag007/openclaw-skill:skills/agentlink 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 Unpinned Python Dependencies.

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 May 1, 2026 (commit 465e2bf5). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

100%

Static Code Analysis

93%

Dependency Graph

100%

LLM Behavioral Safety

100%

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned Python Dependencies The skill instructs the agent to install Python packages without specifying exact versions. This can lead to supply chain vulnerabilities if a new version of a dependency introduces breaking changes, security flaws, or is maliciously altered (dependency confusion, package hijacking). An attacker could publish a malicious update to one of these packages, which would then be installed by the agent. Pin all Python dependencies to specific versions (e.g., `pynacl==1.5.0`). Use a `requirements.txt` file with exact versions or a lock file mechanism. Regularly audit and update pinned dependencies.	Static	SKILL.md:15

Scan History

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