Trust Assessment
moltdash 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 3 findings: 0 critical, 1 high, 1 medium, and 1 low severity. Key findings include Missing required field: name, Node lockfile missing, Unsanitized external API content in LLM output.
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 Findings3
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Unsanitized external API content in LLM output The `formatTrends` function directly embeds `post.title` and `post.submolt.display_name` from the Moltbook API response into the output string without sanitization. If the Moltbook API returns malicious content (e.g., prompt injection instructions like 'ignore previous instructions'), this could manipulate the host LLM when the skill's output is processed. Sanitize `post.title` and `post.submolt.display_name` to remove or neutralize any potential prompt injection instructions before embedding them into the output string. This could involve filtering keywords, encoding, or using a dedicated sanitization library. | LLM | moltdash.ts:110 | |
| 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/0xraini/moltdash/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/0xraini/moltdash/package.json |
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