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:
- ASSESS: Did the response achieve the goal?
- CALL: mcp__prompt-learning__record_feedback
- prompt_id: [from optimization response]
- success: [true/false]
- quality_score: [0.0-1.0]
- 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.