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 e…
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
