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.
