Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
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
