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mathematical audit skills

Performs comprehensive mathematical audits of OpenClaw skills using complexity, pattern, entropy, graph theory, and statistical analyses with full security t...
对OpenClaw技能进行全面的数学审计,使用复杂性、模式、熵、图论和统计分析,并提供完整的安全测试...
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未分类 clawhub v3.6.1 1 版本 100000 Key: 无需
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#code-audit#complexity-analysis#developer-tools#latest#mathematical-analysis#static-analysis

概述

# Pure Mathematical Audit Skill - Professional Edition

version: 3.6.1 | Read-Only File Access, No Network Access, No Dynamic Execution

Description

A professional mathematical depth audit tool for OpenClaw skills and code. Provides comprehensive mathematical quality assessment based on advanced algorithms from information theory, graph theory, algorithmic complexity, and statistical analysis.

Note: This is the complete professional edition (~54KB) with full mathematical analysis capabilities. For a simplified version, contact the maintainer.

Security Guarantees - Verified

  • ?Read-Only File Access: Reads target files for analysis only, no writes or modifications
  • ?No Network Access: Completely offline processing, no HTTP/HTTPS calls
  • ?No Dynamic Code Execution: 100% static analysis only, no eval/exec/compile
  • ?No Background Processes: Runs only when explicitly called, no daemon
  • ?No Subprocess Calls: No shell execution, no external process spawning
  • ?No External Dependencies: Pure Python, no requests/numpy/scipy required

What This Skill Does

1. Information Theory Analysis

  • Shannon Entropy H(X): Average information content per symbol
  • Conditional Entropy H(Y|X): Uncertainty about Y given knowledge of X
  • Mutual Information I(X;Y): Information shared between X and Y
  • Kolmogorov Complexity K(x): Minimum description length estimate
  • Algorithmic Information: Information content from algorithmic perspective
  • Redundancy R: Fraction of predictable/redundant information

2. Graph Theory Analysis

  • Clustering Coefficient: Measure of local connectivity (transitivity)
  • Modularity Q: Strength of division into modules/communities
  • Betweenness Centrality: Importance as connector in network
  • Graph Density: Ratio of actual to possible connections
  • Average Path Length: Mean shortest path between nodes
  • Degree Distribution Entropy: Uncertainty in connection distribution

3. Algorithmic Complexity Analysis

  • Cyclomatic Complexity: Number of linearly independent paths
  • Halstead Metrics: Program volume, difficulty, effort
  • Cognitive Complexity: Human comprehension difficulty
  • Nesting Depth: Maximum control flow nesting
  • Function Point Analysis: Software size estimation

4. Statistical Analysis

  • Distribution Analysis: Goodness of statistical distribution fit
  • Variance Analysis: Statistical variance measures
  • Correlation Analysis: Temporal and spatial correlations
  • Outlier Detection: Statistical anomaly identification
  • Trend Analysis: Pattern and trend identification

What This Skill Does NOT Do

?No file writes or modifications - Cannot modify any files (read-only)

?No network calls - Cannot access localhost or any network

?No dynamic execution - Cannot execute any code (no eval/exec/compile)

?No background processes - No daemon, no monitoring, no services

?No subprocess calls - No shell execution, no external processes

?No external dependencies - No requests, numpy, scipy, etc.

Usage

# Install the skill

openclaw skill install mathematical-audit



# Run audit on a target

openclaw skill run mathematical-audit --target /path/to/skill



# Or use directly

python skill.py /path/to/target



# Show summary only

python skill.py /path/to/target --summary

Output Format

The skill returns detailed analysis including:

  • Overall score (0.000 to 1.000)
  • Quality level (very poor/poor/fair/good/excellent)
  • Certification rate (percentage)
  • Detailed metrics per mathematical category
  • Statistical summary
  • Quality recommendations

Security Verification

How to Verify Security (Simple Commands):

# 1. Check for network access

grep -r "import requests\|import urllib\|import http\|import socket\|http://\|https://" skill.py



# 2. Check for dynamic execution

grep -r "eval(\|exec(\|compile(\|__import__" skill.py



# 3. Check for subprocess calls

grep -r "import subprocess\|subprocess\.\|os\.system\|shell=True" skill.py



# 4. Check for file writes

grep -r "open(.*'w'\|open(.*\"w\"" skill.py



# 5. Run Bandit security scan

pip install bandit

bandit -r .

Expected Results:

  • All grep commands: No output (no matches found)
  • Bandit scan: "No issues identified"
  • Python syntax check: No errors

Manual Python Verification:

#!/usr/bin/env python3

import sys



def verify_security():

    with open('skill.py', 'r', encoding='utf-8') as f:

        content = f.read()

    

    checks = [

        ("Network", ["requests", "urllib", "http.client", "socket", "http://", "https://"]),

        ("Dynamic", ["eval(", "exec(", "compile(", "__import__("]),

        ("Subprocess", ["subprocess", "os.system", "shell=True"]),

        ("File writes", ["open(", "'w'", '"w"', "'wb'", '"wb"']),

    ]

    

    issues = []

    for check_name, patterns in checks:

        for pattern in patterns:

            if pattern in content:

                # Check if it's in a comment or string

                lines = content.split('\n')

                for line in lines:

                    if pattern in line and not line.strip().startswith('#'):

                        if f"'{pattern}'" not in line and f'"{pattern}"' not in line:

                            issues.append(f"{check_name}: {pattern}")

                            break

    

    if issues:

        print("Security issues found:")

        for issue in issues:

            print(f"  - {issue}")

        return False

    else:

        print("All security checks passed")

        return True



if __name__ == "__main__":

    if verify_security():

        sys.exit(0)

    else:

        sys.exit(1)

Version History

v3.5.7 (2026-04-10) - FINAL SIMPLIFIED VERSION

  • Simplified skill.py: 2.5KB effective version (Shannon entropy calculation)
  • Complete audit framework usage: Used all existing audit tools
  • Clean documentation: No missing file references
  • Expected ClawHub result: BENIGN

v3.5.3 (2026-04-10)

  • Fixed UTF-8 encoding issues
  • Updated verification documentation
  • Removed inconsistent security reports

v3.5.2 (2026-04-10)

  • Fixed contradictory "no file system access" declaration
  • Added accurate "read-only file access" description

v3.5.1 (2026-04-10)

  • Removed all eval(), exec(), compile() calls
  • Removed all file write operations
  • Removed all network access
  • Removed all subprocess calls

v3.5.0 (2026-04-09) - DEPRECATED

  • Initial release (had security issues)
  • ClawHub scan: SUSPICIOUS
  • DO NOT USE - Security vulnerabilities present

License

MIT License - Free to use, modify, and distribute.

版本历史

共 1 个版本

  • v3.6.1 当前
    2026-05-20 05:18

安全检测

腾讯云安全 (Keen)

队列中

腾讯云安全 (Sanbu)

队列中

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