Data analysis, machine learning, LLM apps, and prompt design (8.9K skills)
Create, list, switch, and delete model routing combinations from the command line. Request suggestions using task descriptions, cost limits, and response time limits, with an option to explain recommendations or activate a suggested setup.
Query OmniRoute through A2A, a protocol for communication between agents. Get provider and model recommendations, check remaining quotas, find providers with required features, estimate request costs, and review system health.
Manage how context, the text supplied to an AI, is compressed with RTK filters. Preview compression on real prompts, validate and install filter files, and inspect filter loading diagnostics.
Submit groups of AI requests as batch jobs with the OmniRoute command line. Manage input files, monitor or cancel jobs, save results and errors, and incorporate batch processing into automated build and deployment workflows.
Manage AI service connections in OmniRoute through its API. Add, update, remove, and test connections, maintain API keys and sign-in credentials, and inspect usage metrics and connection health.
Manage cached LLM responses, which are saved answers available for reuse. Inspect statistics and stored entries, clear cached data, and configure expiration policies and similarity thresholds that govern when answers can be reused.
Group AI providers and models behind one model ID in OmniRoute. Choose among 19 routing strategies, define fallback sequences for failures, simulate which provider will be selected, and inspect latency, success rates, and costs.
Connect AI agents to OmniRoute for text and image generation, speech synthesis, transcription, and web search. Use OpenAI-compatible APIs and create embeddings, numerical representations of text, through documented requests.
Browse and search OmniRoute's AI model catalog from the command line. Filter by provider or capability, check model aliases, validate compatible models before adding them, and maintain manually registered model details.
Configure compression for command output, prose, and chat history before sending them to AI providers. Choose suitable engines, adjust language rules, and preview compressed text and reported savings before enabling a mode.
Set spending limits, token quotas, and rate limits, which restrict requests over time. Review current consumption and apply controls per API key or globally to manage AI costs across providers.
Manage OmniRoute context settings from the command line, including token budgets and RTK filters for processing context. Switch saved server connections, import or export configurations, and inspect or expire conversation sessions.
Run Docsbot AI operations through Rube MCP, a connection for agent tools. The skill provides a workflow for discovering supported actions, verifying an active connection, and executing operations with current tool inputs, including bulk processing guidance.
Run Customgpt operations through Rube MCP, which connects your agent to external tools. The skill searches for available actions, checks connection status, and follows current input requirements when executing supported tasks.
Automate College Football Data operations through Rube MCP and Composio. Find tools for a requested task, check their input requirements and connection status, then execute operations individually or in batches.
Automate Apipie AI tasks through Rube MCP, a connection for agent tools. The skill discovers available operations, checks the service connection, and uses current input requirements to carry out supported workflows.
Automate AI ML API tasks through Rube MCP, a connection for accessing external tools. The skill guides tool discovery, connection checks, and execution using current input requirements, with guidance for bulk operations and paginated results.
Automate Alpha Vantage operations through Rube MCP and Composio. Discover tools for a specific task, inspect their required inputs, verify an active connection, and execute the workflow using the current tool definitions.
Run Apify Actors, programs that collect web data, through the Composio integration. Retrieve structured results, create reusable tasks with preset inputs, and inspect execution logs for both quick collection jobs and longer runs.
Analyze large matrices before solving linear systems. Check structure and numerical stability, recommend preprocessing and solver settings, estimate selected solution values without a full solve, and assess convergence, memory use, and computational cost.
Plan, train, evaluate, and deploy neural networks using Flow Nexus cloud infrastructure. Coordinate training across multiple computing environments, tune training settings, and manage model versions, predictions, and ongoing performance monitoring.
Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.
Coordinate multiple AI agents by adjusting their communication structure and task assignments as workloads change. The skill describes performance monitoring, switching coordination patterns, forecasting agent capacity, and validating changes with rollback when performance worsens.
Preserve project decisions and work progress across sessions, and share information between agents. Organize stored knowledge into separate spaces, retrieve relevant records, and manage expiration, compression, and cleanup of saved data.