Trust Assessment
1st-commandment 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 Unsafe deserialization / dynamic eval, Missing required field: name, Node lockfile missing.
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 13, 2026 (commit 13146e6a). 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 | Unsafe deserialization / dynamic eval Decryption followed by code execution Remove obfuscated code execution patterns. Legitimate code does not need base64-encoded payloads executed via eval, encrypted-then-executed blobs, or dynamic attribute resolution to call system functions. | Manifest | skills/snail3d/1st-commandment/scripts/calendar-guardian.js:4 | |
| 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 | skills/snail3d/1st-commandment/SKILL.md:1 | |
| LOW | Node lockfile missing package.json is present but no lockfile was found (package-lock.json, pnpm-lock.yaml, or yarn.lock). Commit a lockfile for deterministic dependency resolution. | Dependencies | skills/snail3d/1st-commandment/package.json | |
| INFO | Use of Sensitive Environment Variables The skill loads sensitive environment variables, specifically `GITHUB_TOKEN` and `TELEGRAM_CHAT_ID`, from the `.env` file. The `GITHUB_TOKEN` is subsequently used by the `PatternLearner` component to authenticate requests to the GitHub API. While this usage appears legitimate for the skill's intended functionality (monitoring GitHub activity), the presence and use of these credentials in the skill's runtime environment mean they are a potential target if the skill's code or execution environment were to be compromised. Secure management of these credentials is crucial. Ensure that sensitive environment variables are managed securely, ideally using a dedicated secrets management system rather than directly in `.env` files, especially in production environments. Implement strict access controls to the skill's execution environment and its configuration files. Regularly rotate API tokens and ensure they have the minimum necessary permissions. Avoid logging these tokens or exposing them in any output. | LLM | scripts/1st-commandment.js:28 |
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