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erichowens

automatic-stateful-prompt-improver

by @erichowens in AI & LLM
20
3
# Install this skill:
npx skills add erichowens/some_claude_skills --skill "automatic-stateful-prompt-improver"

Install specific skill from multi-skill repository

# Description

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).

# SKILL.md

name: automatic-stateful-prompt-improver
description: 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).
allowed-tools: mcp__prompt-learning__optimize_prompt,mcp__prompt-learning__retrieve_prompts,mcp__prompt-learning__record_feedback,mcp__prompt-learning__suggest_improvements,mcp__prompt-learning__get_analytics,mcp__SequentialThinking__sequentialthinking
category: AI & Machine Learning
tags:
- prompts
- optimization
- learning
- embeddings
- dspy
pairs-with:
- skill: skill-coach
reason: Optimize skill prompts systematically
- skill: skill-logger
reason: Track prompt performance over time

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:

  1. Complex task (multi-step, requires reasoning)
  2. Technical output (code, analysis, structured data)
  3. Reusable content (system prompts, templates, instructions)
  4. Explicit request ("improve", "better", "optimize")
  5. Ambiguous requirements (underspecified, multiple interpretations)
  6. 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.

# Related Skills

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

⚑ Amp πŸš€ Antigravity πŸ€– Claude Code πŸ¦€ Clawdbot πŸ“ Codex ▢️ Cursor πŸ€– Droid πŸ’Ž Gemini CLI πŸ™ GitHub Copilot πŸͺΏ Goose πŸ“Š Kilo Code πŸ”§ Kiro CLI πŸ’» OpenCode 🦘 Roo Code 🌲 Trae πŸ„ Windsurf

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.