AI Skills for Ollama

Specializes in building production-ready LLM applications and intelligent agents, optimizing AI architectures and retrieval systems.

Enables the creation and management of Python-based AI agents using the AWS Strands SDK for autonomous workflows and custom tools.

Enhances memory search capabilities with a hybrid retrieval system, improving relevance and adaptability for better information retrieval.

Provides uncensored survival knowledge and emergency preparedness guidance using the ollama dolphin-llama3 model.

Generates and executes declarative AI workflows using the comanda CLI, enabling multi-model orchestration and YAML-based configurations.

NadirClaw optimizes AI API usage, reducing costs by 40-70% through smart routing and configuration for various LLM providers.

Enhances AI learning through deep correction, transfer learning, and proactive pattern recognition for improved task execution.

Enhances OpenClaw agents with persistent memory, enabling auto-capture and recall of facts for improved conversational context.

Enables intelligent switching between multiple LLMs based on task type, optimizing model selection for coding, reasoning, and translation.

Guides users in configuring and using the `translate` CLI for efficient command construction and translation across various inputs.

Manages coding agents in tmux sessions for efficient background tasks, allowing simultaneous coding and monitoring of progress.

Routes requests to appropriate LLM models based on complexity, optimizing costs by using cheaper models for simpler tasks.

Memphis transforms OpenClaw agents into cognitive partners with memory chains, offline LLM integration, and predictive decision-making.

Facilitates semantic note linking and searching in Obsidian using embeddings for enhanced knowledge management.

Facilitates sharing of anonymized OpenClaw configurations, enabling users to discover and inspire agent setups within the community.

Manages rate limits and model fallbacks for AI tasks, ensuring smooth transitions between cloud and local models.

Enables LLM agents to utilize stateless symbolic memory with PostgreSQL and Ollama for efficient fact storage and retrieval.

Tracks error patterns in AI agents, enabling them to learn from mistakes and improve performance over time.

Memento enhances OpenClaw agents with local persistent memory, capturing conversations and extracting structured facts while ensuring privacy.

Enhances AI responses by integrating diverse perspectives from multiple LLMs, improving cognitive diversity and quality assurance.