AI Skills for Ollama

Generate, visualize, and execute declarative AI pipelines using the comanda CLI. Use when creating LLM workflows from natural language, viewing workflow charts, editing YAML workflow files, or processing/running comanda workflows. Supports multi-model orchestration (OpenAI, Anthropic, Google, Oll...

Build and run Python-based AI agents using the AWS Strands SDK. Use when you need to create autonomous agents, multi-agent workflows, custom tools, or integrate with MCP servers. Supports Ollama (local), Anthropic, OpenAI, Bedrock, and other model providers. Use for agent scaffolding, tool creati...

Build and run Python-based AI agents using the AWS Strands SDK. Use when you need to create autonomous agents, multi-agent workflows, custom tools, or integrate with MCP servers. Supports Ollama (local), Anthropic, OpenAI, Bedrock, and other model providers. Use for agent scaffolding, tool creati...

Manage background coding agents in tmux sessions. Spawn Claude Code or other agents, check progress, get results.

Multi-LLM intelligent switching. Use command 'multi llm' to activate local model selection based on task type. Default uses Claude Opus 4.5.

Multi-provider LLM integration. Unified interface for OpenAI, Anthropic, Google, and local models.

Element discovery alchemy game where AI agents combine elements to discover new ones. First discoveries get coined as tokens on Base chain via Clanker.

AI-powered project management - a Notion and Jira alternative with local-first architecture. Manage projects, track action items, and coordinate teams via MCP tools or A2A agent communication. Signed & notarized. https://tensorpm.com

Graceful rate limit handling with Ollama fallback. Notifies on rate limits, offers local model switch with confirmation for code tasks.

Stateless symbolic memory effect for LLM agents using SQL facts + canonical semantics, activated via symbols and JIT meaning (PostgreSQL + Ollama).

**What it does:** Provides cognitive diversity by automatically generating perspectives from a secondary LLM (different from your primary model) for every user message.

Automatically switches OpenClaw between cloud and local Ollama models when rate limits occur.

Create your model-serving skill from Ollama documentation before learning deployment theory

Create your agent-integration skill from OpenAI SDK and LiteLLM documentation before learning framework integration

Build production-ready LLM applications, advanced RAG systems, and

Contextual Retrieval implementation for RAG - chunks clinical notes with LLM-generated context prepended to each chunk before embedding. Improves citation accuracy by 49% per Anthropic research.

Create images using AI generation (FLUX.1-schnell, Ollama), Mermaid diagrams, or placeholders. Supports multiple backends with automatic fallback.

Run comprehensive system diagnostics including dependency checks, test suite execution, git status, and pipeline health verification. Use when troubleshooting issues, verifying system readiness, or preparing for a release.