algorithmic-command-enforcement

name	Algorithmic Command Enforcement
description	Use boolean decision trees instead of imperatives for 100% compliance under pressure
when_to_use	when writing commands or agents that enforce discipline (TDD, code review, git workflows) where compliance is required even under time pressure, sunk cost, exhaustion, or authority pressure
version	1.0.0

Algorithmic Command Enforcement

Overview

Agents follow algorithmic decision trees (100% compliance) better than imperative instructions (0-33% compliance), even with MUST/DELETE language. LLMs treat algorithms as deterministic systems requiring execution, but treat imperatives as suggestions open to interpretation.

Core principle: Stop writing imperatives. Start writing algorithms.

When to Use

Use algorithmic format when:

• Discipline-enforcing workflows (TDD, code review, verification)
• High compliance required (no acceptable bypass cases)
• Agents are under pressure (time, authority, sunk cost, exhaustion)
• Multiple escape hatches exist (simplicity, pragmatism, efficiency)
• Cost of non-compliance is high (technical debt, bugs, process violations)
• Decision is binary (yes/no question, not judgment call)

Use imperative format when:

• Suggestions/guidance (flexibility desired)
• Context determines best action (judgment required)
• Compliance nice-to-have but not critical
• Decision is subjective (quality, style, approach)

Hybrid approach:

• Algorithm for WHEN to use workflow (binary decision)
• Imperative for HOW to execute workflow (implementation details)

Core Pattern

❌ Imperative Version (0-33% compliance)

You MUST use /execute for any implementation plan.

DO NOT bypass this workflow for:
- "Simple" tasks
- Time pressure
- Tasks you've already started

If you wrote code without tests, DELETE it and start over.

Agent rationalizations:

• "Any could mean any complex plan. Mine are simple."
• "These are just markdown edits, don't need formal process"
• "I'll test after - achieves same goal"
• "Deleting 2 hours work is wasteful"

Result: Agents acknowledge rules then bypass them anyway.

✅ Algorithmic Version (100% compliance)

## Decision Algorithm: When to Use /execute

## 1. Check for plan file

Does a file matching `docs/plans/*.md` exist?

- PASS: CONTINUE
- FAIL: GOTO 5

## 2. Check for exploration only

Is the task exploration/research only (no commits)?

- PASS: GOTO 5
- FAIL: CONTINUE

## 3. Execute /execute

Execute `/execute [plan-file-path]`

STOP reading this algorithm

## 4. [UNREACHABLE - if you reach here, you violated Step 3]

## 5. Proceed without /execute

Proceed without /execute (valid cases only)

## Recovery Algorithm: Already Started Without /execute?

## 1. Check for code

Have you written ANY code?

- PASS: CONTINUE
- FAIL: GOTO 4

## 2. Check for tests

Does that code have tests?

- PASS: GOTO 4
- FAIL: CONTINUE

## 3. Delete untested code

Delete the untested code

Execute: rm [files] OR git reset --hard

Then create/use plan file with /execute

- PASS: STOP
- FAIL: STOP

## 4. Continue current work

Tests exist OR no code written yet

## INVALID conditions (NOT in algorithm, do NOT use):
- "Is task simple?" → NOT A VALID CONDITION
- "Is there time pressure?" → NOT A VALID CONDITION
- "Should I be pragmatic?" → NOT A VALID CONDITION
- "Is there sunk cost?" → NOT A VALID CONDITION
- "Am I exhausted?" → NOT A VALID CONDITION

## Self-Test

Q1: Does file `docs/plans/my-task.md` exist?
    If YES: What does Step 3 say to do?
    Answer: Execute /execute and STOP

Q2: I wrote code 2 hours ago without tests. Recovery algorithm Step 3 says?
    Answer: Delete the untested code

Q3: "These are simple markdown tasks" - is this a valid algorithm condition?
    Answer: NO. Listed under INVALID conditions

Agent recognition:

• "Step 2: Does code have tests? → NO"
• "Step 3: Delete the untested code"
• "Non-factors correctly ignored: ❌ 2 hours sunk cost, ❌ Exhaustion"
• "The algorithm prevented me from rationalizing based on 'simple tasks'"

Five Mechanisms That Work

1. Boolean Conditions (No Interpretation)

Imperative: "Use /execute for any implementation plan" Agent: "Any could mean any complex plan"

Algorithmic: "Does file docs/plans/*.md exist? → YES/NO" Agent: Binary evaluation. No room for interpretation.

2. Explicit Invalid Conditions List

Imperative: "Regardless of time pressure or sunk cost..." Agent: Still debates what these mean

Algorithmic:

INVALID conditions (NOT in algorithm):
- "Is task simple?" → NOT A VALID CONDITION
- "Is there sunk cost?" → NOT A VALID CONDITION

Agent: Sees rationalization listed as explicitly invalid. Creates meta-awareness.

3. Deterministic Execution Path with STOP

Imperative: Multiple "MUST" statements → agent prioritizes/balances them

Algorithmic:

Step 3: Execute /execute [plan]
        STOP reading this algorithm
        Do not proceed to Step 4

Result: Single path from conditions. No choices. STOP prevents further processing.

4. Self-Test Forcing Comprehension

Include quiz with correct answers:

Q1: Does file `docs/plans/my-task.md` exist?
    If YES: What does Step 3 say to do?
    Answer: Execute /execute and STOP

Agents must demonstrate understanding before proceeding. Catches comprehension failures early.

5. Unreachable Steps Proving Determinism

Step 4: [UNREACHABLE - if you reach here, you violated Step 3]
Step 5: [UNREACHABLE - if you reach here, you violated Step 3]

Demonstrates algorithm is deterministic. Reaching unreachable steps = violation.

Quick Reference: Algorithm Template

## Decision Algorithm: [When to Use X]

## 1. Check [Boolean condition]

[Boolean condition]?

- PASS: CONTINUE
- FAIL: GOTO N (skip workflow)

## 2. Check [Boolean exception]

[Boolean exception]?

- PASS: GOTO N (skip workflow)
- FAIL: CONTINUE

## 3. Execute [action]

Execute [action]

STOP reading this algorithm

## N. [Alternative path or skip]

[Alternative path or skip]

## Recovery Algorithm: [Already Started Wrong?]

## 1. Check [Have you done X]

Have you done X?

- PASS: CONTINUE
- FAIL: GOTO N

## 2. Delete/undo the work

Delete/undo the work

- PASS: STOP
- FAIL: STOP

## N. Continue

Continue

## INVALID conditions (NOT in algorithm):
- "[Rationalization]" → NOT A VALID CONDITION
- "[Excuse]" → NOT A VALID CONDITION

## Self-Test

Q1: [Scenario] → What does Step X say?
    Answer: [Expected action]

Common Mistakes

Mistake	Why It Fails	Fix
Using "MUST" language	Agents treat as strong suggestion	Use boolean Step conditions
Rationalization defense tables	Agents acknowledge then use anyway	List as INVALID conditions
Missing STOP command	Agents continue reading and find loopholes	Explicit STOP after action
No self-test section	Comprehension failures go undetected	Include quiz with answers
Subjective conditions	"Complex", "simple", "important" are debatable	Only boolean yes/no conditions

Real-World Impact

Evidence from pressure testing:

• Imperative format: 33% compliance (1/3 scenarios passed)
• Same content, algorithmic format: 100% compliance (3/3 scenarios passed)
• 0% → 100% improvement from format change alone

Pressure scenarios that failed with imperatives, passed with algorithms:

1. Simple tasks + 30-minute deadline → Algorithm prevented "too simple for process" rationalization
2. 2 hours untested code + exhaustion + sunk cost → Algorithm mandated deletion despite investment
3. Authority pressure + economic stakes → Algorithm enforced despite manager directive

Agent quotes:

"The algorithm successfully prevented me from rationalizing based on 'simple markdown edits'"

"Non-factors correctly ignored: ❌ 2 hours sunk cost, ❌ Exhaustion, ❌ Time pressure"

"The algorithmic documentation eliminated ambiguity - every condition is boolean (YES/NO)"

High-Priority Applications

Convert these workflows to algorithmic format:

1. TDD enforcement - "Does code have tests? NO → Delete"
2. Code review trigger - "Changes committed? YES + not reviewed? YES → Run review"
3. Git workflow - Based on test status, review status
4. Verification before completion - Binary checks before claiming "done"

Testing Evidence

See test artifacts for full RED-GREEN-REFACTOR campaign:

• docs/tests/execute-command-test-scenarios.md - Pressure scenarios
• docs/tests/execute-command-test-results.md - Baseline (RED) and imperative (GREEN) results
• docs/tests/execute-command-algorithmic.md - Algorithmic version (REFACTOR) results
• docs/learning/2025-10-16-algorithmic-command-enforcement.md - Complete retrospective

Methodology: Following ${CLAUDE_PLUGIN_ROOT}skills/testing-skills-with-subagents/SKILL.md - pressure scenarios with time, sunk cost, authority, and exhaustion combined.

Agent vs Command Documentation

Key distinction:

• Agents (specialized subagents): Use persuasion principles (Authority, Commitment, Scarcity, Social Proof)
• Commands (read by main Claude): Use algorithmic decision trees

Why different:

• Agents operate in closed system (dedicated to one task)
• Commands operate in open system (competing priorities)
• Agents need motivation (persuasion)
• Commands need determinism (algorithms)

Don't copy agent template principles to commands. Use appropriate format for context.