Finance Paper Daily

• Outcome: Pass
• Latest audit: May 28, 2026 · 2:38 PM UTC
• Version: 1.0.0

Overview

This skill appears to fetch public finance paper metadata and create a desktop Excel report, with disclosed but notable dependency-installation risk.

Install this only if you are comfortable with it contacting public academic services and creating or overwriting ~/Desktop/YYYY-MM-DD_finance_papers.xlsx. Prefer running it in a virtual environment and preinstalling pinned dependencies yourself instead of using --break-system-packages or allowing runtime pip installs.

SkillSpector

Vulnerability Patterns

• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep
• Behavioral AST: exec() Call, eval() Call, Dynamic Import
• MCP Least Privilege: Underdeclared Capability, Wildcard Permission, Missing Permission Declaration
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands

Findings (8)

subprocess module call

Medium

• Category: Dangerous Code Execution

Content

except ImportError:
    print('正在安装 openpyxl...')
    import subprocess
    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'openpyxl', '-q'])
    import openpyxl
    from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
    from openpyxl.utils import get_column_letter

• Confidence: 96% confidence
• Finding: The script automatically invokes pip to install openpyxl at runtime, which modifies the host environment without explicit user approval. Even though the package name is hardcoded and not shell-injected, runtime package installation expands the skill's privileges from data fetching into software installation and creates supply-chain and environment-tampering risk.

subprocess module call

Medium

• Category: Dangerous Code Execution

Content

from scholarly import scholarly as sc
        except ImportError:
            import subprocess
            subprocess.check_call([sys.executable,'-m','pip','install','scholarly','-q'])
            from scholarly import scholarly as sc
        keywords = ['quantitative finance 2024','asset pricing machine learning 2024']
        seen = set()

• Confidence: 96% confidence
• Finding: This path auto-installs the scholarly package when Google Scholar support is used, again modifying the local Python environment at runtime without prior consent. That adds unnecessary host-modification capability for a paper-reporting skill and exposes the system to dependency supply-chain risk.

Lp3

Medium

• Category: MCP Least Privilege
• Confidence: 92% confidence
• Finding: The skill clearly instructs users to install packages and run a Python script that uses both shell execution and external network access, yet no permissions are declared. This creates a transparency and consent problem: the agent can fetch remote content and modify the environment without an explicit permission model, increasing the risk of unintended system changes or abuse if the script is altered.

Context-Inappropriate Capability

Medium

• Confidence: 97% confidence
• Finding: Installing Python packages at runtime is outside the narrow necessity of fetching papers and generating an Excel report, so the skill gains host-modification capability beyond its stated purpose. In this context, that is a real security concern because it turns a read/report workflow into one that can alter system state and pull executable code from package repositories.

Context-Inappropriate Capability

Medium

• Confidence: 97% confidence
• Finding: The Google Scholar branch introduces an unjustified environment-changing action by installing scholarly at runtime. Because this dependency is optional for one source, the risk is less defensible: the skill could still function without altering the host, making the added capability unnecessary and dangerous.

Missing User Warnings

Low

• Confidence: 89% confidence
• Finding: The skill states that it will save an Excel report directly to the user's Desktop and overwrite the same-day file, but it does not prominently warn users that local files will be created or replaced. While the described behavior is expected for the skill's purpose, silent modification of user files can still surprise users and cause minor data loss or trust issues.

Missing User Warnings

Medium

• Confidence: 95% confidence
• Finding: The script installs openpyxl via pip without prior warning or confirmation, so users are not informed that running the skill will change their environment. That violates least surprise and can bypass organizational controls on software installation, especially in managed or sensitive environments.

Missing User Warnings

Medium

• Confidence: 95% confidence
• Finding: The auto-installation of scholarly occurs without clearly disclosing that the skill will download and install software. This is dangerous because an apparently simple paper-fetching action can unexpectedly modify the host and introduce third-party executable code.

Static analysis

No suspicious patterns detected.