ai-ml-technologies
Master AI, machine learning, LLMs, prompt engineering, and blockchain development. Use when building AI applications, working with LLMs, or developing smart contracts.
prompt-hacking
Advanced prompt manipulation including direct attacks, indirect injection, and multi-turn exploitation
flux-kontext-prompt-engineer
Expert prompt engineering for FLUX.1 Kontext image generation and editing. Use when users request AI image generation, image editing, style transformations, or visual content modifications. This skill covers both text-to-image generation and image-to-image editing capabilities.
prompt-engineering-patterns
Master 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.
anthropic-architect
Determine the best Anthropic architecture for your project by analyzing requirements and recommending the optimal combination of Skills, Agents, Prompts, and SDK primitives.
PRP Generator
Guides creation of Product Requirements Prompts (PRPs) - comprehensive requirement documents that serve as the foundation for AI-assisted development
image-gen
Generate images using Google's Nano Banana Pro (Gemini 3 Pro Image) with workflow-based prompting
promptup
Transform vague requirements into production-grade prompts using evidence-based principles. Diagnose prompt issues, define boundaries, and iterate to quality.
subagent-generator
Generates custom Claude Code subagents with specialized expertise. Activates when user wants to create a subagent, specialized agent, or task-specific AI assistant. Creates properly formatted .md files with YAML frontmatter, suggests tool restrictions and model selection, generates effective system prompts. Use when user mentions "create subagent", "new agent", "specialized agent", "task-specific agent", or wants isolated context for domain-specific work.
create-subagent
Guide for creating specialized Claude Code subagents with proper YAML frontmatter, focused descriptions, system prompts, and tool configurations. Use when the user wants to create a new subagent, custom agent, specialized AI assistant, or mentions creating/designing/building agents or subagents.
prompt-engineering
Expert skill for prompt engineering and task routing/orchestration. Covers secure prompt construction, injection prevention, multi-step task orchestration, and LLM output validation for JARVIS AI assistant.
llm-integration
Expert skill for integrating local Large Language Models using llama.cpp and Ollama. Covers secure model loading, inference optimization, prompt handling, and protection against LLM-specific vulnerabilities including prompt injection, model theft, and denial of service attacks.
vim-ai-config
Manage vim-ai plugin configuration files including .vimrc settings, custom roles, API keys, and model configurations. Use when user requests help with vim-ai setup, modifying vim-ai behavior, adding custom roles, changing AI models, updating prompts, or troubleshooting vim-ai issues.
oracle
Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.
Generative Framework
Conversation-driven specification and execution of healthcare data generation at scale
prompt-engineering
Designs and optimizes prompts for large language models to achieve better, more consistent outputs. Trigger keywords: prompt, LLM, GPT, Claude, prompt engineering, AI prompts, few-shot, chain of thought.
prompt-optimizer
This skill should be used when users request help optimizing, improving, or refining their prompts or instructions for AI models. Use this skill when users provide vague, unclear, or poorly structured prompts and need assistance transforming them into clear, effective, and well-structured instructions that AI models can better understand and execute. This skill applies comprehensive prompt engineering best practices to enhance prompt quality, clarity, and effectiveness.
story-based-framing
This skill should be used when describing patterns or anti-patterns for detection by LLM agents across any domain (code analysis, business processes, security audits, UX design, data quality, medical diagnosis, etc.). Uses narrative storytelling structure ("The Promise" → "The Betrayal" → "The Consequences" → "The Source") to achieve 70% faster pattern identification compared to checklist or formal specification approaches. Triggers when creating pattern descriptions for any systematic analysis, detection tasks, or when delegating pattern-finding to sub-agents.
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