llm-cli
Process textual and multimedia files with various LLM providers using the llm CLI. Supports both non-interactive and interactive modes with model selection, config persistence, and file input handling.
llm-council
Multi-LLM collaborative brainstorming and planning. Use when user explicitly requests consultation with multiple AI models (ChatGPT, Gemini, other LLMs) before presenting an implementation plan, or asks to "consult the council", "ask other models", or "get perspectives from other AIs". Queries external LLM APIs, synthesizes their perspectives, and presents an adapted implementation plan.
Convex Agents Context
Customizes what information the LLM receives for each generation. Use this to control message history, implement RAG context injection, search across threads, and provide custom context.
ralph-driven-development-linear
Ralph Driven Development workflow that pulls Linear project issues via the Linear MCP and runs them sequentially with Codex. Use when automating task execution from a Linear project and when you need a runner that advances issues and asks for the next task automatically.
ai-native-development
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
building-ai-agent-on-cloudflare
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model-mediated-development
Use when building any system that involves AI/model calls - integrates with brainstorming, planning, and TDD to ensure model agency over hardcoded rules
pydantic-ai-agent-creation
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
pydantic-ai-model-integration
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
llm-judge
LLM-as-judge methodology for comparing code implementations across repositories. Scores implementations on functionality, security, test quality, overengineering, and dead code using weighted rubrics. Used by /beagle:llm-judge command.
council-orchestrator
Orchestrates multi-model LLM consensus through a three-phase deliberation protocol. Use when you need collaborative AI review, multi-model problem-solving, code review from multiple perspectives, or consensus-based decision making. Coordinates OpenAI Codex, Google Gemini, and Claude CLIs for opinion collection, peer review, and chairman synthesis.
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.
model-detection
Universal model detection and capability assessment for optimal cross-model compatibility
context-engineering
Master context engineering for AI features - the skill that separates AI products that work from ones that hallucinate. Use when speccing new AI features, diagnosing underperforming AI features, or doing quality checks before shipping. Helps PMs define what context AI needs, where to get it, and what to do when it fails. Based on the 4D Context Canvas framework.
council
Run multi-LLM council for adversarial debate and cross-validation. Orchestrates Claude, GPT-4, and Gemini for production-grade implementation, code review, architecture design, research, and security analysis.
openai
OpenAI API via curl. Use this skill for GPT chat completions, DALL-E image generation, Whisper audio transcription, embeddings, and text-to-speech.
nuxt-content
Use when working with Nuxt Content v3 - provides collections (local/remote/API sources), queryCollection API, MDC rendering, database configuration, NuxtStudio integration, hooks, i18n patterns, and LLMs integration
openai-assistants
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