Ai-Ml plugins

Multi-agent system optimization, agent improvement workflows, and context management

2 commands · 1 agent

Multi-agent coordination patterns for enterprise AI on Oracle Cloud

1 skill · 1 command

Build production agentic applications on OCI using Oracle Agent Development Kit

1 skill · 1 command

Design framework-agnostic AI agents using Oracle's Open Agent Specification

1 skill · 1 command

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.4

8 skills · 3 commands · 3 agents

11 skills for the Clarifai AI platform: model init/serve/deploy, inference, MCP servers, pipelines, datasets, observability, and more.

11 skills

ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows

2 skills · 1 command · 3 agents

Multi-agent orchestration with AI SDK v5 - handoffs, routing, and coordination for any AI provider (OpenAI, Anthropic, Google)

1 skill · 3 commands · 1 agent

Firebase Genkit expert for production-ready AI workflows with RAG and tool calling

1 skill · 1 command · 1 agent

AI ethics and fairness validation

1 skill · 1 command

LLM application development, prompt engineering, and AI assistant optimization

4 skills · 3 commands · 2 agents

ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows

1 skill · 1 command · 3 agents

Multi-agent system optimization, agent improvement workflows, and context management

2 commands · 1 agent

Frame ML problems, engineer and select features, train and tune scikit-learn, XGBoost, LightGBM and CatBoost models, cluster and reduce dimensions, validate without leakage, explain models with SHAP, PDP, LIME and counterfactuals, and build forecasting, survival, recommendation, ranking, fraud and uplift models.

159 skills

First Principles Framework (FPF) for structured, auditable reasoning with hypothesis-driven decision making and evidence trails

1 skill · 13 commands · 1 agent

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

LLM application development: RAG systems, prompt engineering, AI agents, and production LLM patterns

9 skills · 3 commands · 2 agents

LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.4

8 skills · 3 commands · 3 agents

LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.4

8 skills · 3 commands · 3 agents

LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.4

8 skills · 3 commands · 3 agents

Drive corerun from an agent: submit and debug training jobs, fine-tune with LoRA, manage datasets and the model registry, serve models, and work with the endpoints applications call. Every skill drives the corerun CLI, so anywhere the CLI is installed and logged in, these work.

14 skills

LLM application development with LangGraph, RAG systems, vector search, and agents built on LangChain 1.x

8 skills · 3 commands · 3 agents

LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.4

8 skills · 3 commands · 3 agents

LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.4

8 skills · 3 commands · 3 agents

LLM application development, prompt engineering, and AI assistant optimization

4 skills · 3 commands · 2 agents