Security Audit
distributed-training-ray-train
github.com/davila7/claude-code-templatesTrust Assessment
distributed-training-ray-train received a trust score of 83/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 4 findings: 0 critical, 0 high, 2 medium, and 1 low severity. Key findings include Missing required field: name, Network egress to untrusted endpoints, Covert behavior / concealment directives.
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 12, 2026 (commit 458b1186). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings4
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Missing required field: name The 'name' field is required for claude_code skills but is missing from frontmatter. Add a 'name' field to the SKILL.md frontmatter. | Static | cli-tool/components/skills/ai-research/distributed-training-ray-train/SKILL.md:1 | |
| MEDIUM | Network egress to untrusted endpoints HTTP request to raw IP address Review all outbound network calls. Remove connections to webhook collectors, paste sites, and raw IP addresses. Legitimate API calls should use well-known service domains. | Manifest | cli-tool/components/mcps/devtools/figma-dev-mode.json:4 | |
| LOW | Covert behavior / concealment directives Multiple zero-width characters (stealth text) Remove hidden instructions, zero-width characters, and bidirectional overrides. Skill instructions should be fully visible and transparent to users. | Manifest | cli-tool/components/mcps/devtools/jfrog.json:4 | |
| INFO | Unpinned dependency in installation instructions The `pip install` command suggests installing `ray[train]` without a specific version. While `ray[train]` is a legitimate package, installing unpinned dependencies can lead to non-deterministic environments, compatibility issues, or unexpected behavior if a new version introduces breaking changes or vulnerabilities. It is a best practice to pin dependencies to specific versions (e.g., `ray[train]==X.Y.Z`) for reproducibility and stability. Pin the dependency to a specific version in the installation instructions, for example: `pip install -U "ray[train]==2.40.0"` (or the desired stable version). | LLM | SKILL.md:10 |
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