Trust Assessment
scholar-evaluation received a trust score of 78/100, 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, 3 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/scientific/scholar-evaluation/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 | |
| MEDIUM | Path Traversal Vulnerability in Score Calculation Script The `scripts/calculate_scores.py` script reads and writes files based on paths provided via command-line arguments (`--scores`, `--output`, `--weights`). If an AI agent executes this script with user-controlled input for these arguments, an attacker could supply paths like `../../../../etc/passwd` to read arbitrary files or `../../../../tmp/malicious_report.txt` to write to arbitrary locations, leading to information disclosure or arbitrary file overwrite. The `SKILL.md` explicitly instructs to 'use' this script programmatically, indicating it's intended for execution by the agent. Implement robust path sanitization (e.g., `os.path.abspath`, `os.path.normpath`, or checking if the resolved path is within an allowed, designated directory) for all user-provided file paths before opening them for reading or writing. Ensure that the resolved path does not escape the intended working directory. | LLM | scripts/calculate_scores.py:32 | |
| 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 |
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