automatic-stateful-prompt-improver
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).
npx skills add majiayu000/claude-skill-registry --skill automatic-stateful-prompt-improver-curiositech-some-claude-skill --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ## Lea
- MANDATORY AUTOMATIC BEHAVIOR
- Auto-Optimization Triggers
- Auto-Optimization Process
- Do NOT Optimize
- Learning Loop (Post-Response)
- Quick Reference
- Iteration Decision
- Convergence (When to Stop)
- Performance Expectations
- Anti-Patterns
- Over-Optimization
- Template Obsession
- Iteration Without Measurement
- Ignoring Model Capabilities
What does the automatic-stateful-prompt-improver skill do?
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).
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill automatic-stateful-prompt-improver-curiositech-some-claude-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.