Does this skill help with security audits?

Yes, it is specifically designed to check against standards, verify session handling, test edge cases, and identify risks that standard quality checks might overlook.

What is the 150% quality standard?

It is a commitment to excellence that combines 100% core task delivery with an additional 50% effort focused on strengthening the output through verification, alternative analysis, and risk awareness.

How does this differ from standard AI code generation?

Standard generation often relies on fragments or assumptions; this skill forces the AI to read full files, verify every assumption with code evidence, and provide multiple implementation options.

What is a confidence score in this context?

At the end of a task, the skill provides a numerical percentage representing the AI's certainty based on what was verified versus what remains assumed or limited.

When should I use the Execute-Quality 150 skill?

Invoke this skill for critical tasks like production deployments, security-sensitive changes, complex multi-system problems, or major architectural decisions where you cannot afford errors.

Execute-Quality 150 Protocol

Name: Execute-Quality 150 Protocol
Author: MykhailoDmytriakha

byMykhailoDmytriakha

生産性とワークフロー

Executes critical development tasks with a 150% quality standard by combining core delivery with rigorous verification and risk assessment.

概要

The Execute-Quality 150 Protocol is an advanced execution framework for Claude Code designed for high-stakes scenarios where standard AI output is insufficient. It mandates a comprehensive workflow that includes reading full file contexts instead of snippets, validating all technical assumptions with evidence, considering at least three alternative approaches, and performing continuous risk mitigation. Ideal for production deployments, security audits, and complex architectural decisions, this skill ensures that AI-generated solutions are not only functional but thoroughly vetted, documented, and delivered with explicit numerical confidence levels.

主な機能

0 GitHub stars
Multi-perspective analysis requiring at least three alternative solutions
Integrated risk assessment and failure mode mitigation
Full context reading of entire files, imports, and dependencies
Detailed delivery reports with explicit numerical confidence scores
Evidence-based verification of all underlying technical assumptions

ユースケース

Reviewing security-critical authentication or encryption logic
Architecting complex multi-system integrations with high dependencies
Debugging production-level issues where the cost of error is high

概要

主な機能

0 GitHub stars
Multi-perspective analysis requiring at least three alternative solutions
Integrated risk assessment and failure mode mitigation
Full context reading of entire files, imports, and dependencies
Detailed delivery reports with explicit numerical confidence scores
Evidence-based verification of all underlying technical assumptions

ユースケース

Reviewing security-critical authentication or encryption logic
Architecting complex multi-system integrations with high dependencies
Debugging production-level issues where the cost of error is high