Skill UI

Provides interpretable explanations for ML predictions using SHAP, LIME, and other techniques, helping debug model behavior, highlight feature importance, and communicate fairness insights to stakeholders.

Automates Flask-based machine learning API deployment by guiding you through best practices, generating production code/configurations, and validating outputs for serving, monitoring, and MLOps pipelines.

Guide to reducing Groq inference spend by routing requests to cost-effective models, trimming token usage, caching repeated calls, batching requests, and setting spend limits for Groq Cloud billing scenarios.

Sets up Groq LLM API clients for development, staging, and production tiers with tailored models, retries, and rate-limit handling so deployments stay aligned with each environment’s speed and cost profile.

This skill guides the combination of vector similarity search and keyword-based matching to achieve superior information retrieval. It is essential when building advanced Retrieval-Augmented Generation (RAG) systems, custom search engines, or when standard search methods fail to capture both semantic meaning and specific term requirements.

Guides selection of Ideogram models, resolutions, caching, batching, and monitoring to lower per-image credits while maintaining quality; ideal for billing analysis or budget-aware teams using Ideogram image generation.

Build asynchronous Kling AI video generation workflows with queues, workers, and state machines to orchestrate event-driven pipelines and monitor job progress within larger systems.

Knowledge Distillation is a crucial technique used to compress large, high-performing Language Models (Teacher) into smaller, more efficient Student models. This allows developers to deploy powerful AI capabilities (like those of GPT-4) on resource-constrained devices while significantly reducing inference costs. The method uses advanced techniques such as soft targets, temperature scaling, and Reverse KL Divergence (MiniLLM) to transfer knowledge effectively.

Provides production-ready LangChain SDK patterns for chains, agents, and memory, covering type-safe outputs, fallbacks, async batching, streaming, caching, and structured error handling to keep integrations reliable.

Monitor Langfuse telemetry to record LLM token usage, aggregate spend by model and time, alert on budget breaches, and steer requests toward cheaper models while caching responses and trimming prompts.

This skill guides users through advanced prompt optimization techniques for Large Language Models (LLMs). It functions as an expert prompt engineer coach, applying methodologies like Constitutional AI and Chain-of-Thought reasoning. Use it to transform basic, raw instructions into highly effective, production-ready prompts, significantly improving accuracy, reducing hallucinations, and optimizing token usage for cost efficiency.

Automates Mailcoach operations through Composio’s Rube MCP toolkit by first discovering current tool schemas, managing the mailcoach connection, and executing schema-compliant workflows safely with session and memory handling.