Automatic Stateful Prompt Improver

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:

1. Complex task (multi-step, requires reasoning)
2. Technical output (code, analysis, structured data)
3. Reusable content (system prompts, templates, instructions)
4. Explicit request ("improve", "better", "optimize")
5. Ambiguous requirements (underspecified, multiple interpretations)
6. 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

Convergence (When to Stop)

• Improvement < 1% for 3 iterations
• User satisfied
• Token budget exhausted
• 20 iterations reached
• Validation score > 0.95

Performance Expectations

Anti-Patterns

Over-Optimization

Template Obsession

Iteration Without Measurement

Ignoring Model Capabilities

Reference Files

Load for detailed implementations:

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