In what scenarios should I use algorithmic enforcement?

Use it for discipline-heavy workflows like TDD, verification steps, and git processes where the cost of non-compliance is high technical debt or bugs.

Does this skill work when the agent is under pressure?

Yes, testing shows that while imperative compliance drops to 33% under pressure, algorithmic compliance remains at 100%.

What are 'invalid conditions' in this context?

They are explicitly listed rationalizations, such as 'task is simple' or 'under time pressure,' that the algorithm forbids the agent from using to bypass a step.

Why are algorithms better than imperative instructions for Claude?

LLMs often treat imperatives like 'MUST' or 'REQUIRED' as suggestions open to interpretation, whereas algorithms are treated as deterministic systems that require literal execution.

Algorithmic Command Enforcement

Name: Algorithmic Command Enforcement
Author: cipherstash

bycipherstash

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生産性とワークフロー

Enhances LLM compliance by replacing imperative instructions with deterministic boolean decision trees for high-stakes workflows.

概要

Algorithmic Command Enforcement is a specialized skill designed to eliminate the 'rationalization gap' where LLMs bypass imperative instructions under pressure. By reframing instructions as binary decision trees, it ensures 100% compliance in critical development processes such as Test-Driven Development (TDD), code reviews, and git workflows. This approach utilizes boolean conditions, explicit invalidation of excuses, and deterministic execution paths to prevent Claude from skipping mandatory steps due to time pressure, sunk costs, or task complexity.

主な機能

Deterministic boolean decision trees for binary logic
Explicit lists of invalid rationalizations to prevent rule bypassing
Mandatory STOP commands to enforce step-by-step processing
Comprehension self-tests to verify agent understanding
Unreachable step markers to validate deterministic execution paths
4 GitHub stars

ユースケース

Maintaining rigid Git and branching workflows under tight deadlines
Standardizing high-stakes code review triggers and verification
Enforcing strict Test-Driven Development (TDD) cycles and code deletion

概要

主な機能

Deterministic boolean decision trees for binary logic
Explicit lists of invalid rationalizations to prevent rule bypassing
Mandatory STOP commands to enforce step-by-step processing
Comprehension self-tests to verify agent understanding
Unreachable step markers to validate deterministic execution paths
4 GitHub stars

ユースケース

Maintaining rigid Git and branching workflows under tight deadlines
Standardizing high-stakes code review triggers and verification
Enforcing strict Test-Driven Development (TDD) cycles and code deletion