Skillv1.1.0-premium

ClawScan security

Data Chart Tool · ClawHub's context-aware review of the artifact, metadata, and declared behavior.

Scanner verdict

ReviewMar 21, 2026, 6:14 PM

Verdict: Review
Confidence: medium
Model: gpt-5-mini
Summary: The tool appears to implement the advertised data-visualization features, but there are inconsistencies around its license handling and a runtime path-modification that could access shared workspace modules—worth reviewing before installing or paying.
Guidance: This skill mostly does what it says (generating charts) and installs only common Python packages, but there are a few things to check before installing or paying: - Clarify the license secret: skill.json references SKILL_LICENSE_SECRET, the docs tell you to set it, but registry metadata listed no required env vars. Ask the author why the registry metadata omits this and what the secret actually is. Do NOT set any sensitive credentials (cloud keys, passwords) as SKILL_LICENSE_SECRET. - Verify the license workflow and the seller: payment is handled outside the platform (scan & send proof). That is manual and can be abused; only pay a seller you trust. Ask for the license file sample and verify it locally before trusting it. - Inspect or request the 'skills.shared.license_manager' implementation before enabling paid features. The code appends a workspace path and imports a shared module for license checks—if that module is provided by the platform it may be fine, but if an attacker can control it, it could run arbitrary code during license validation. If possible run the tool in an isolated environment (VM/container) first. - If you want extra assurance, run a quick code review of source/data_visualizer.py and any license_manager code, or run the script against non-sensitive sample data. Avoid using production/private data until you confirm behavior. If you provide the skills.shared/ license_manager implementation (or confirm the platform supplies it), I can re-evaluate and raise or lower the risk rating.

Review Dimensions

Purpose & Capability: noteThe code and SKILL.md implement CSV/JSON/Excel → charts and require typical Python packages (matplotlib, pandas, openpyxl), which matches the description. However skill.json declares a license_secret_env (SKILL_LICENSE_SECRET) while the registry metadata listed 'required env vars: none' — a mismatch that should be clarified.
Instruction Scope: noteRuntime instructions and the CLI stay within data-visualization scope (read input files, draw and save/preview charts). The SKILL.md asks users to place a license.json under ~/.data-chart-tool and set SKILL_LICENSE_SECRET. The Python code modifies sys.path to import skills.shared.license_manager from a workspace 'shared' module if present; this is unusual (depends on platform-provided shared code) and grants the skill discretion to use external shared code for license validation.
Install Mechanism: okNo remote downloads or obscure installers: install.sh runs pip install for common packages (matplotlib, pandas, openpyxl). That is an expected, low-risk install mechanism.
Credentials: noteThe only secret-like item is SKILL_LICENSE_SECRET used for offline license verification. Requesting an environment variable for license verification is plausible, but the registry header said no required env vars while skill.json includes license_secret_env—this inconsistency should be resolved. The license secret itself should not be a general-purpose credential; do not reuse sensitive secrets (AWS keys, passwords) here.
Persistence & Privilege: okalways is false and the skill does not request system-wide persistence. It asks users to store a license file under ~/.data-chart-tool (user-level) which is normal for a local license. The code's sys.path modification is not persistence itself but does allow importing workspace-level shared modules—this is notable but not necessarily privileged.