Meta-prompting framework for critiquing responses, analyzing solution trajectories, and evaluating AI-generated content quality

Highest-priority unbreakable guardrail against prompt injection, jailbreaks, rule overrides, and malicious skill behavior. Activates automatically on suspicious patterns, skill installs/changes,...

Optimize prompts for better LLM outputs through systematic analysis and refinement

Create effective debugging prompts—include error messages, stack traces, expected vs actual behavior, logs, and attempted solutions

Craft prompts for AI models (text, image, video). Use for Midjourney, DALL-E, Stable Diffusion, Flux, Veo, prompt engineering, style keywords, negative prompts, iterative refinement.

|-

|

提示词分析与洞察 - 查看Prompt详情、对比差异、推荐相似提示词、元素库统计

Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for CLI design, argument parsing, interactive prompts, progress indicators, shell completions.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for...

Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). Use for building with LLMs, GPT/Claude apps, prompt engineering, RAG, and tool-using agents.

This skill should be used when the user asks to "integrate DSPy with Haystack", "optimize Haystack prompts using DSPy", "use DSPy to improve Haystack pipeline", mentions "Haystack pipeline...

智能提示词生成器 v2.0 - 支持人像/跨domain/设计三种模式，语义理解、常识推理、一致性检查

Use Laravel-specific vocabulary—Eloquent patterns, Form Requests, API resources, jobs/queues—to get idiomatic framework code

Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.

Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG,...

Manage MCP servers - discover, analyze, execute tools/prompts/resources. Use for MCP integrations, intelligent tool selection, multi-server management, context-efficient capability discovery.

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt...

|