codex-claude-loop
Orchestrates a dual-AI engineering loop where Claude Code plans and implements, while Codex validates and reviews, with continuous feedback for optimal code quality
Найдите подходящую возможность для вашего агента.
Orchestrates a dual-AI engineering loop where Claude Code plans and implements, while Codex validates and reviews, with continuous feedback for optimal code quality
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation. Auto-invokes planning-pipeline after design. Includes agent mapping for intelligent subagent orchestration during execution.
Create specialized Claude Code sub-agents with custom system prompts and tool configurations. Use when users ask to create a new sub-agent, custom agent, or task-specific AI workflows.
Data science and machine learning platform functions for the East language (TypeScript types). Use when writing East programs that need optimization (MADS, Optuna, SimAnneal, Scipy), machine learning (XGBoost, LightGBM, NGBoost, Torch MLP, Lightning, GP), ML utilities (Sklearn preprocessing, metrics, splits), conformal prediction (MAPIE), or model explainability (SHAP). Triggers for: (1) Writing East programs with @elaraai/east-py-datascience, (2) Derivative-free optimization with MADS, (3) Bayesian optimization with Optuna, (4) Discrete/combinatorial optimization with SimAnneal, (5) Gradient boosting with XGBoost or LightGBM, (6) Probabilistic predictions with NGBoost or GP, (7) Neural networks with Torch MLP or Lightning, (8) Data preprocessing and metrics with Sklearn, (9) Conformal prediction intervals with MAPIE, (10) Model explainability with Shap.
Build CADSL-based cognitive artifacts for SEA-Forge™ including checklists, planners, decision trees, kanban boards, and mind maps. Use for generating interactive, semantically-anchored artifacts that support knowledge work and collaborative decision-making. Aligns with Cognitive Architecture patterns and Knowledge Graph integration.
Master prompt injection attacks, jailbreak techniques, input manipulation, and payload crafting for LLM security testing
Beginner workflow for LlamaIndex agents (Python). Use when the user wants FunctionAgent or ReActAgent with tools.
Request human approval for a critical operation via Discord MCP server. Posts a message with reaction buttons and uses await_reaction to wait for response. Use for destructive or irreversible operations. Keywords: approval, Discord, human-in-the-loop, confirmation, dangerous operation, reactions.
Use when building AI agents with Google's Agent Development Kit (ADK) Python - multi-agent systems, workflow agents, tool integration, Vertex AI deployment, or agent evaluation.
Intelligent skill suggestion engine that analyzes user intent and project context to recommend appropriate documentation skills
Create, clean, and optimize datasets for LLM fine-tuning. Covers formats (Alpaca, ShareGPT, ChatML), synthetic data generation, quality assessment, and augmentation. Use when preparing data for training.
Trace and evaluate GenAI applications including LLM calls, agents, RAG pipelines, and multi-step AI systems in Domino. Uses the Domino SDK (@add_tracing decorator, DominoRun context) with MLflow 3.2.0. Captures token usage, latency, cost, tool calls, and errors. Supports LLM-as-judge evaluators and custom metrics. Use when building agents, debugging LLM applications, or needing audit trails for GenAI systems.
Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.
Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps. Use when building AI models, data pipelines, or machine learning systems.
Techniques to extract model weights, architecture, and training data through API queries
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use when working with LLM features, chatbots, AI-powered applications, or agentic systems.
タスクの性質を LLM ベースで深く分析し、適切な executor(claudecode/codex/coderabbit/user)を判定する専門 Skill。キーワードベースの単純判定を置き換える。
Beginner workflow for OpenAI Agents SDK (Python or TypeScript). Use when the user wants a code-first agent app with tools, handoffs, streaming, and traces.
Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering.
Analyze Context Lake entities and fix frontmatter issues. Use when asked to run frontmatter cleanup, fix missing fields, validate frontmatter against schema, or standardize entity metadata.