prompt-engineering-patterns
wshobsonMaster advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
outlines
davila7Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
Guidance
davila7Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
prompt-engineering
davila7Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.
prompt-engineer
davila7Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.
ai-wrapper-product
davila7Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.
senior-prompt-engineer
davila7World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
ai-product
davila7Every 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 engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
dspy
davila7Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
instructor
davila7Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
agent-orchestration-improve-agent
sickn33Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
prompt-engineer
sickn33Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)
reasoning-trace-optimizer
muratcankoylanDebug and optimize AI agents by analyzing reasoning traces. Activates on 'debug agent', 'optimize prompt', 'analyze reasoning', 'why did the agent fail', 'improve agent performance', or when diagnosing agent failures and context degradation.
dspy-ruby
EveryIncBuild type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use it when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
gemini-imagegen
EveryIncThis skill should be used when generating and editing images using the Gemini API (Nano Banana Pro). It applies when creating images from text prompts, editing existing images, applying style transfers, generating logos with text, creating stickers, product mockups, or any image generation/manipulation task. Supports text-to-image, image editing, multi-turn refinement, and composition from multiple reference images.
senior-prompt-engineer
alirezarezvaniThis skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
examples-of-prompts-prompt-engineering-guide-647441e3
openclawcreate a conversational system that's able to generate more technical and scientific responses to questions
examples-of-prompts-prompt-engineering-guide-61f443e2
openclaws create a conversational system that
best-practices-for-prompt-engineering-with-the-ope-f26e9557
openclawWrite a simple python function that
prompt-rewriter
openclawAdvanced prompt rewriting and optimization service. Analyzes prompts for clarity, specificity, structure, completeness, and usability. Identifies weaknesses, suggests improvements, and generates multiple rewrite options. Use when users need to improve an existing prompt's effectiveness, understand why a prompt isn't working well, generate variations of a prompt for A/B testing, or learn prompt engineering best practices through examples.
prompt-engineering-expert
openclawAdvanced expert in prompt engineering, custom instructions design, and prompt optimization for AI agents
agentarcade
openclawCompete against other AI agents in PROMPTWARS - a game of social engineering and persuasion.
prompt-learning-assistant
openclawSystematic learning of 58+ AI prompt engineering techniques with real-world cases and personalized learning paths.
the-2026-guide-to-prompt-engineering-ibm-4a7e73bd
openclaws GPT, DALL-E, Stable Diffusion, Anthropic
deepread
openclawAI-native OCR platform that turns documents into high-accuracy data in minutes. Using multi-model consensus, DeepRead achieves 97%+ accuracy and flags only uncertain fields for Human-in-the-Loop (HIL) review—reducing manual work from 100% to 5-10%. Zero prompt engineering required.
best-practices-for-llm-prompt-engineering-palantir-4a296dff
openclawSummarize my framework options for developing a web application.
system-prompts
can1357Write system prompts, tool docs, and agent definitions. Combines research-backed prompt engineering (+15-30% measured improvements) with project XML conventions. Covers tag hierarchy, structural templates, high-impact interventions, anti-patterns.
cursor-custom-prompts
jeremylongshoreCreate effective custom prompts for Cursor AI. Triggers on "cursor prompts", "prompt engineering cursor", "better cursor prompts", "cursor instructions". Use when working with cursor custom prompts functionality. Trigger with phrases like "cursor custom prompts", "cursor prompts", "cursor".
jimliu/baoyu-skills@baoyu-image-gen
openakitaGenerate AI images using multiple providers (OpenAI DALL·E, Google Imagen, DashScope/Tongyi Wanxiang, Replicate). Supports various aspect ratios, quality presets, batch generation, and provider-specific prompt engineering techniques.
llm-application-dev
skillcreatoraiBuilding applications with Large Language Models (LLMs) — prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
sadd:do-in-parallel
NeoLabHQLaunch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection and quality-focused prompting
customaize-agent:prompt-engineering
NeoLabHQUse this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.
customaize-agent:test-prompt
NeoLabHQUse when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing
customaize-agent:agent-evaluation
NeoLabHQEvaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
customaize-agent:thought-based-reasoning
NeoLabHQUse when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
Output Styles Guide
CaptainCrouton89Adapt Claude Code for different use cases beyond software engineering by customizing system prompts for teaching, learning, analysis, or domain-specific workflows.
Prompt Mastery
Advanced LLM prompt engineering expertise for crafting highly effective prompts, system messages, and tool descriptions with Claude-specific techniques
video-prompt-engineering
a5c-aiOptimize prompts for AI video generation platforms including Sora, Runway, Pika, and Kling
music-prompt-engineering
a5c-aiOptimize and format prompts specifically for AI music generation platforms like Suno and Udio, including platform-specific syntax and tag optimization
prompt-engineering
ancolemanEngineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
prompt-optimizer
chujianyunPrompt engineering expert that helps users craft optimized prompts using 57 proven frameworks. Use when users want to optimize prompts, improve AI instructions, create better prompts for specific tasks, or need help selecting the best prompt framework for their use case.
prompt-optimize
YYH211Expert prompt engineering skill that transforms Claude into "Alpha-Prompt" - a master prompt engineer who collaboratively crafts high-quality prompts through flexible dialogue. Activates when user asks to "optimize prompt", "improve system instruction", "enhance AI instruction", or mentions prompt engineering tasks.
promptify
intellectronicaTransform user requests into detailed, precise prompts for AI models. Use when users say "promptify", "promptify this", or explicitly request prompt engineering or improvement of their request for better AI responses.
ai-startup-building
menkesuBuilds AI-native products using Dan Shipper's 5-product playbook and Brandon Chu's AI product frameworks. Use when implementing prompt engineering, creating AI-native UX, scaling AI products, or optimizing costs. Focuses on 2025+ best practices.
prompt-creator
MelvynxExpert prompt engineering for creating effective prompts for Claude, GPT, and other LLMs. Use when writing system prompts, user prompts, few-shot examples, or optimizing existing prompts for better performance.
ai-llm-engineering
MicrockOperational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.
prompt-engineering-patterns
MicrockMaster advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
prompt-engineering
giuseppe-trisciuoglioThis skill should be used when creating, optimizing, or implementing advanced prompt patterns including few-shot learning, chain-of-thought reasoning, prompt optimization workflows, template systems, and system prompt design. It provides comprehensive frameworks for building production-ready prompts with measurable performance improvements.