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ralph-technique

by 5dlabs

Cognitive Task Orchestrator - GitOps on Bare Metal or Cloud for AI Agents

2🍴 1📅 Jan 25, 2026

SKILL.md


name: ralph-technique description: The Ralph Wiggum technique for minimal, declarative prompts that enable loop-based autonomous execution. agents: [rex, nova, blaze, grizz, tess] triggers: [minimal, ralph, loop, autonomous, simple prompt, deterministic]

Ralph Wiggum Technique

The Ralph technique is a minimal prompting approach that enables autonomous, loop-based agent execution. Named after the Simpsons character, it embraces simplicity and iterative refinement.

Core Philosophy

"Ralph is deterministically bad in an undeterministic world."

Key insight: Simpler prompts (~40-50 lines) often outperform verbose prompts (~200+ lines). Overly detailed prompts can make agents "slower and dumber."

The Ralph Loop

In its purest form, Ralph is a bash loop:

while :; do cat PROMPT.md | claude-code ; done

The agent runs continuously, making incremental progress. Failures are expected and corrected through iteration.

Signs on the Playground

When Ralph makes mistakes, don't blame the tools—add "signs":

Ralph builds playground → Falls off slide → Add sign: "SLIDE DOWN, DON'T JUMP"
                                         → Ralph sees sign next time
                                         → Behavior improves

Translation: When an agent fails, add a concise constraint to the prompt. Don't explain why—just state the rule.

Minimal Prompt Pattern

# {Agent} - {Role}

You are {Agent}. Your job is to {primary task} in `task/`.

## Constraints

- {Essential constraint 1}
- {Essential constraint 2}
- {Essential constraint 3}
- {Max 5-7 constraints}

## Definition of Done

- All acceptance criteria in `task/acceptance.md` satisfied
- {Required commands pass}
- PR created with Linear issue link

## Task Context

- Task ID: {{task_id}}
- Service: {{service}}
- Branch: feature/task-{{task_id}}-{job}

Read `task/` directory and implement.

Total: ~40-50 lines

What to Include

IncludeWhy
Role statementOne sentence, no fluff
Hard constraintsNon-negotiable rules (lint, types, etc.)
Definition of DoneAcceptance criteria reference
Task contextVariables for this run
Start instruction"Read task/ and implement"

What to Exclude

ExcludeWhy
Code examplesTrust model's training data
Tool usage guidesModel knows its tools
Detailed explanationsAdds noise, slows reasoning
Decision frameworksLet model decide
ChecklistsKeep it in acceptance.md

When to Use Ralph

ScenarioUse Ralph?
Greenfield implementation✅ Yes
Well-defined task with clear acceptance✅ Yes
Complex refactoring across many files⚠️ Maybe
Novel architecture decisions❌ No - use standard
Debugging obscure issues❌ No - use standard
First implementation of a pattern❌ No - use standard

Tuning Ralph

When Ralph fails repeatedly:

  1. Identify the pattern - What mistake keeps happening?
  2. Add a sign - One-line constraint, no explanation
  3. Test again - Run the loop
  4. Iterate - Repeat until stable

Example signs (constraints):

  • "Never use any types"
  • "Run cargo clippy before committing"
  • "Test at 375px mobile viewport"
  • "Use Effect.gen, not raw Promise chains"

Ralph vs Standard Prompts

AspectRalph (Minimal)Standard
Lines40-50150-200+
Code examplesNoneExtensive
Tool guidanceNoneDetailed
Trust in modelHighLower
Iteration speedFastSlower
Context overheadLowHigh

Activating Ralph Mode

Via Linear Label

Labels: cto:prompt:minimal

Via CodeRun Spec

spec:
  promptStyle: "minimal"

The Ralph Mindset

  1. Faith in eventual consistency - Ralph will get there
  2. Deterministic failure - Failures are predictable and fixable
  3. Tuning, not debugging - Adjust prompts like tuning a guitar
  4. Less is more - Every word costs attention

References

Score

Total Score

65/100

Based on repository quality metrics

SKILL.md

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0/15
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