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shannon

Quantify code quality via SNR, cyclomatic entropy, redundancy, and channel overhead metrics.

1|Updated Jun 4, 2026
Search Tags:#code quality#quality control#code cleanup#quantitative metrics#information theory
One-click install
npx skills add https://github.com/m16khb/agent-harness --skill shannon-m16khb
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shannon
Source: https://github.com/m16khb/agent-harness/tree/main/skills/shannon
Command: npx skills add https://github.com/m16khb/agent-harness --skill shannon-m16khb

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of subjectively evaluating code quality, providing objective, quantifiable measurements to gauge the cleanliness and efficiency of code.

Core Features & Use Cases

  • Code Quality Metrics: Measure SNR, cyclomatic entropy, redundancy, and channel overhead to assess code quality.
  • Reproducibility: Ensures that all measurements are reproducible across commits and sessions.
  • Integration: Seamlessly integrates with IssueOps and other AI coding agents for a cohesive workflow.

Quick Start

Run the shannon skill to measure the quality of your codebase before and after cleanup.

Frequently Asked Questions about shannon

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I quantify code quality objectively for code cleanup workflows?▼

Information theory provides quantitative metrics for code quality by measuring signal-to-noise ratio (SNR), cyclomatic entropy, redundancy, and channel overhead. These metrics apply information theory principles to objectively evaluate code cleanliness and structural complexity.

What is the best way to measure cyclomatic entropy and redundancy in a codebase?▼

You can measure code quality metrics like SNR and cyclomatic entropy by running the analysis tool before and after code cleanup. It requires AST analysis to calculate specific metrics, ensuring reproducible measurements of signal efficiency and redundancy across your commits.

Can I integrate quantitative code quality metrics with IssueOps and AI coding agents?▼

Yes, quantitative code quality metrics integrate seamlessly with IssueOps and AI coding agents. This integration automates quality control within your existing workflow, ensuring objective SNR and redundancy measurements guide code cleanup decisions.

Does measuring code channel overhead require AST analysis?▼

Yes, calculating code quality metrics like channel overhead and cyclomatic entropy requires AST analysis. Abstract Syntax Tree parsing is necessary to accurately measure structural complexity and signal efficiency within the codebase.

How do I ensure code quality measurements are reproducible across different commits?▼

To ensure code quality measurements are reproducible across commits, use quantitative metrics calculated through AST analysis. Measuring SNR, cyclomatic entropy, and channel overhead provides consistent, objective quality control data across different development sessions.

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