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Algorithmic Command Enforcement

Replace imperative commands with boolean decision trees for deterministic execution.

17|Updated Oct 13, 2025
Search Tags:#automation#workflow design#meta-skill#AI compliance#agent behavior#algorithmic enforcement#decision tree
One-click install
npx skills add https://github.com/cipherstash/cipherpowers --skill algorithmic-command-enforcement
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Algorithmic Command Enforcement
Source: https://github.com/cipherstash/cipherpowers/tree/main/plugin/skills/meta/algorithmic-command-enforcement
Command: npx skills add https://github.com/cipherstash/cipherpowers --skill algorithmic-command-enforcement

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Imperative instructions (e.g., "You MUST do X") often lead to low compliance in AI agents due to rationalization. This Skill provides a meta-pattern for writing boolean decision trees that enforce 100% compliance, even under pressure, eliminating ambiguity and ensuring consistent execution.

Core Features & Use Cases

  • Boolean Decision Trees: Replaces vague imperatives with clear YES/NO conditions for deterministic execution.
  • Explicit Invalid Conditions: Lists common rationalizations as explicitly invalid, preventing agents from using them to bypass rules.
  • Deterministic Execution Path: Uses STOP commands to ensure a single, non-negotiable path through the workflow.
  • Use Case: When designing a critical workflow like TDD enforcement, use this Skill's pattern to ensure agents always write tests first and delete untested code, eliminating bypasses due to "simplicity" or "sunk cost."

Quick Start

When designing a new command or agent workflow:

1. Define a "Decision Algorithm" with Step N: Check: [Boolean condition]? -> YES/NO

2. Include a "Recovery Algorithm" for when things go wrong.

3. List "INVALID conditions" to preempt rationalizations.

4. Add a "Self-Test" section to verify comprehension.

Frequently Asked Questions about Algorithmic Command Enforcement

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

FAQPage Schema
How do I enforce compliance in AI agent workflows without rationalization bypass?▼

Replace imperative commands with boolean decision trees that use YES/NO conditions and explicit STOP semantics. This eliminates ambiguity by listing invalid rationalizations upfront, ensuring deterministic execution paths that agents cannot circumvent through pressure, sunk cost, or authority objections.

What's the best way to design a TDD enforcement workflow that ensures tests are always written first?▼

Structure a decision algorithm with boolean conditions (e.g., 'Tests written before code?') and recovery algorithms for failures. Define invalid conditions explicitly—such as 'code is simple' or 'deadline pressure'—and add STOP commands to create a single, non-negotiable execution path.

When should I use algorithmic decision trees instead of imperative instructions?▼

Use this approach for discipline-enforcing workflows like TDD, code reviews, verification, and Git workflows where strict compliance is required despite time pressure or competing priorities. Boolean decision trees achieve 100% compliance by replacing vague imperatives with deterministic logic.

How do I prevent agents from rationalizing away compliance rules?▼

List common rationalizations as explicitly invalid conditions in your decision algorithm. Pair this with STOP semantics to block alternative paths. This transforms compliance from negotiable guidance into a hardcoded execution boundary that agents cannot rationalize past.

Can I use this pattern for workflows beyond code discipline?▼

Yes. The boolean decision tree pattern applies to any workflow requiring 100% compliance under pressure. It works for agent behavior enforcement, meta-skill design, and any process where imperative commands fail due to rationalization, making it broadly applicable across automation and AI compliance contexts.

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