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
code
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:
code
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.