Automatic Stateful Prompt Improver

MANDATORY AUTOMATIC BEHAVIOR

When this skill is active, I MUST follow these rules:

Auto-Optimization Triggers

I AUTOMATICALLY call mcp__prompt-learning__optimize_prompt BEFORE responding when:

• Complex task (multi-step, requires reasoning)

• Technical output (code, analysis, structured data)

• Reusable content (system prompts, templates, instructions)

• Explicit request ("improve", "better", "optimize")

• Ambiguous requirements (underspecified, multiple interpretations)

• Precision-critical (code, legal, medical, financial)

Auto-Optimization Process

1. INTERCEPT the user's request
2. CALL: mcp__prompt-learning__optimize_prompt
   • prompt: [user's original request]
   • domain: [inferred domain]
   • max_iterations: [3-20 based on complexity]
3. RECEIVE: optimized prompt + improvement details
4. INFORM user briefly: "I've refined your request for [reason]"
5. PROCEED with the OPTIMIZED version

Do NOT Optimize

• Simple questions ("what is X?")

• Direct commands ("run npm install")

• Conversational responses ("hello", "thanks")

• File operations without reasoning

• Already-optimized prompts

Learning Loop (Post-Response)

After completing ANY significant task:

1. ASSESS: Did the response achieve the goal?
2. CALL: mcp__prompt-learning__record_feedback
   • prompt_id: [from optimization response]
   • success: [true/false]
   • quality_score: [0.0-1.0]
3. This enables future retrievals to learn from outcomes

Quick Reference

Iteration Decision

Factor Low (3-5) Medium (5-10) High (10-20)

Complexity Simple Multi-step Agent/pipeline

Ambiguity Clear Some Underspecified

Domain Known Moderate Novel

Stakes Low Moderate Critical

Convergence (When to Stop)

• Improvement < 1% for 3 iterations

• User satisfied

• Token budget exhausted

• 20 iterations reached

• Validation score > 0.95

Performance Expectations

Scenario Improvement Iterations

Simple task 10-20% 3-5

Complex reasoning 20-40% 10-15

Agent/pipeline 30-50% 15-20

With history +10-15% bonus Varies

Anti-Patterns

Over-Optimization

What it looks like Why it's wrong

Prompt becomes overly complex with many constraints Causes brittleness, model confusion, token waste

Instead: Apply Occam's Razor - simplest sufficient prompt wins

Template Obsession

What it looks like Why it's wrong

Focusing on templates rather than task understanding Templates don't generalize; understanding does

Instead: Focus on WHAT the task requires, not HOW to format it

Iteration Without Measurement

What it looks like Why it's wrong

Multiple rewrites without tracking improvements Can't know if changes help without metrics

Instead: Always define success criteria before optimizing

Ignoring Model Capabilities

What it looks like Why it's wrong

Assumes model can't do things it can Over-scaffolding wastes tokens

Instead: Test capabilities before heavy prompting

Reference Files

Load for detailed implementations:

File Contents

references/optimization-techniques.md

APE, OPRO, CoT, instruction rewriting, constraint engineering

references/learning-architecture.md

Warm start, embedding retrieval, MCP setup, drift detection

references/iteration-strategy.md

Decision matrices, complexity scoring, convergence algorithms

Goal: Simplest prompt that achieves the outcome reliably. Optimize for clarity, specificity, and measurable improvement.