AI & LLM Integration for Claude Code

Execute AI/ML task with intelligent automation

Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with \"design training pipeline\", \"tune model hyperparameters\".

Execute AI/ML task with intelligent automation

Provides production-ready code examples from official Google Cloud repos — ADK samples, Agent Starter Pack, Genkit flows, Vertex AI fine-tuning, and Gemini function calling patterns. Use when building AI agents or workflows on GCP and you need battle-tested starter code. Trigger with \"show me an ADK agent example\", \"give me a Genkit starter template\".

Designs and implements production-grade Firebase Genkit flows (Node.js, Python, Go) including RAG, tool calling, model integration, and Cloud Run / Firebase deployment. Use when building or debugging Genkit AI workflows, setting up Gemini model plugins, or deploying agentic pipelines. Trigger with \"create a Genkit flow\", \"design AI workflow with Genkit\".

Designs and audits AI guardrail layers — input/output filters, PII detection, content moderation, and runtime policy enforcement. Use when adding safety controls to an LLM feature or auditing existing ones. Trigger with \"design guardrails\", \"audit our AI safety controls\".

List all available workflow nodes

Generate production-ready LLM API integration boilerplate

Delivers production-ready LLM API integration patterns including retry/backoff, rate limiting, multi-provider fallback, streaming, and cost tracking across OpenAI, Anthropic, and Google. Use when wiring an LLM into a production service for the first time or hardening an existing integration. Trigger with "integrate an LLM API", "production LLM setup".

Log a new AI experiment with terminal report

Design Make.com scenarios with AI assistance

Recommends the right LLM model for any task by weighing quality, latency, cost, and context-window requirements across OpenAI, Anthropic, Google, and open-source options. Use when choosing a model for a new feature or reducing spend without sacrificing quality. Trigger with "which model should I use", "pick the best LLM for this".

Generate complete n8n workflow JSON files

Execute AI/ML task with intelligent automation

Execute AI/ML task with intelligent automation

Execute AI/ML task with intelligent automation

Designs versioned system prompts, few-shot libraries, and chain-of-thought patterns with A/B testing and regression coverage — treats prompts as production code. Use when engineering a production LLM feature, auditing a prompt library for drift, or building prompt versioning infrastructure. Trigger with \"design this system prompt\", \"audit our prompt library\".

Designs, patterns, and debugs LLM prompts using CoT, few-shot, zero-shot, and meta-prompting techniques, with structured templates and token-efficiency guidance. Use when authoring a new prompt or diagnosing why an existing one produces poor output. Trigger with "design a prompt", "help me write a prompt".

Analyze and improve a plugin prompt, skill definition, or command instruction

Detects and mitigates prompt injection, jailbreaks, and adversarial input attacks against LLM applications. Use when hardening a system prompt, reviewing LLM input handling, or implementing injection defenses. Trigger with \"defend against prompt injection\", \"harden llm inputs\".

Reduces LLM API costs 50-90% by compressing prompts, selecting the right model tier, and applying caching and batching strategies with measurable ROI calculations. Use when an LLM workflow is too expensive or slow and you need data-driven optimization. Trigger with "optimize this prompt", "reduce my LLM costs".

> Generate reusable prompt templates with variables and best practices

Implements production-grade Python/FastAPI backends with async patterns, PostgreSQL/Alembic migrations, auth, and LLM integrations strictly from sprint API contract and backend specs, returning a BACKEND IMPLEMENTATION REPORT. Use when building or updating Python API services in a sprint. Trigger with "implement backend sprint", "build FastAPI endpoint".

Designs production RAG pipelines covering chunking strategy, embedding selection, retrieval patterns (basic, reranked, hybrid, multi-query), and evaluation metrics. Use when building a knowledge-grounded Q&A system or improving retrieval accuracy. Trigger with "design a RAG system", "help me build a knowledge base".

> Generate complete RAG pipeline with embeddings, vector DB, and retrieval

Designs retrieval reranking pipelines, relevance scoring systems, and learning-to-rank models with rigorous NDCG/MRR evaluation. Use when ranking quality is poor or a reranker is needed. Trigger with \"improve my search ranking\", \"design a reranking pipeline\".

Generate comprehensive terminal report of AI experiments

Execute AI/ML task with intelligent automation

Execute AI/ML task with intelligent automation

Search AI experiments and display formatted results

Install and configure Ollama for local AI models

Execute AI/ML task with intelligent automation

Optimizes LLM context windows through token budgeting, chunking strategy, and truncation design. Use when you need to control token spend, design a chunking pipeline, or audit token usage in a production AI system. Trigger with \"design my token budget\", \"fix my context overflow\".

Instruments LLM systems with tracing, span capture, cost attribution, and debugging tooling. Use when you need to add observability to AI pipelines, debug model behavior, or audit LLM cost attribution. Trigger with \"instrument my LLM calls\", \"debug my AI system with traces\".

Execute AI/ML task with intelligent automation

Execute AI/ML task with intelligent automation

Train a machine learning model with specified parameters

Complete AI-powered travel planner with real-time weather, budget...