davila7/claude-code-templates

AI Agents Architect designs and builds autonomous AI agents, covering tool use, memory systems, planning strategies, and multi-agent orchestration. Use it when building or debugging AI agents that need function calling, planning loops, and controlled autonomy.

Agent Evaluation tests and benchmarks LLM agents using behavioral contracts, capability assessments, reliability metrics, and adversarial testing to catch issues before production. Use it when evaluating agent reliability, designing benchmarks, or monitoring production agents.

Agent Management creates, manages, and orchestrates AI agents through the AI Maestro CLI, covering agent lifecycle tasks such as create, hibernate, wake, rename, export/import, and plugin management.

Agent Manager starts, stops, monitors, and assigns tasks to multiple local CLI agents running in tmux sessions, with cron-friendly scheduling. Use it when you need to run agents in parallel and tail their logs.

Agent Memory MCP provides a persistent, searchable memory bank for AI agents, exposing MCP tools to search, write, read, and analyze project knowledge. Use it when an agent needs long-term memory synced with project documentation.

Agent Memory Systems describes architectures for short-term, long-term, and working memory in AI agents, including vector store selection, chunking strategies, and retrieval patterns. Use it when designing or debugging agent memory to prevent retrieval failures that look like intelligence failures.

Agent Messaging sends and receives cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to send a message to an agent, check agent inbox, message another agent, reply to a message, notify an agent, or any inter-agent communication task.

Agent Tool Builder teaches how to design tools for AI agents, including JSON Schema, descriptions, validation, and MCP standards, so agents work reliably.

AutoGPT Agents provides a platform for building, deploying, and managing continuous autonomous AI agents through a visual workflow builder or development toolkit. Use it when creating visual workflow agents, deploying persistent agents with webhooks or schedules, or building complex multi-step AI automation systems.

Autonomous Agent Patterns describes design patterns for building autonomous coding agents, including tool/function calling APIs, permission systems, browser automation, and human-in-the-loop workflows. Use it when designing AI agents or autonomous coding assistants.

Autonomous Agents teaches patterns for building reliable AI agents that decompose goals, plan actions, execute tools, and self-correct without constant human guidance. Use it when designing agent loops like ReAct or Plan-Execute and adding guardrails and reliability measures for production.

Behavioral Modes defines distinct AI operating modes (brainstorm, implement, debug, review, teach, ship, orchestrate) that adapt Claude's behavior based on task type. Use it to switch modes automatically or manually for more effective responses.

Claude Code Guide provides templates and best practices for configuring Claude Code, prompting the agent, and debugging issues. The guide includes configuration templates, thinking keywords, and debugging techniques.

Computer Use Agents build AI agents that interact with computers like humans do — viewing screens, moving cursors, clicking buttons, and typing text. Use it when building desktop automation, vision-based control, or GUI agents with sandboxing and security.

Context Window Management provides strategies for managing LLM context windows, including summarization, trimming, routing, and avoiding context rot. Use it when hitting token limits, losing critical information mid-dialogue, or tuning long-context applications.

CrewAI Multi-Agent orchestrates teams of autonomous AI agents that collaborate on complex tasks through role-based delegation and sequential or hierarchical workflows. Use it when building multi-agent systems with memory, tools, and observability without LangChain dependencies.

Design to Code converts Figma designs into production-ready React and TypeScript components using a helper script and coderio. Use it for high-fidelity UI restoration from Figma to React with error handling and checkpoint recovery.

Docs Search queries auto-generated codebase documentation to find function signatures, class definitions, API docs, and code comments. Use it before implementing changes or when asked to search docs or look up a function.

GMod Addon Maker assists with creating and managing Garry's Mod addons, including Lua scripting, content creation, and addon packaging. Use it when developing new addons, writing Lua scripts, debugging, organizing files, or packaging for distribution.

Graph Query queries a code graph database to understand component relationships, dependencies, and change impact. Use when asked to find callers, check dependencies, or understand what depends on a component before modifications.

LangChain is a framework for building LLM-powered applications with agents, chains, and RAG. Use it for chatbots, question-answering systems, autonomous agents, and rapid prototyping across multiple providers.

LlamaIndex is a data framework for building LLM applications with RAG, specializing in document ingestion, indexing, and querying. Use it for document Q&A, chatbots, knowledge retrieval, and building RAG pipelines over private data.

Memory Search searches conversation history and semantic memory to recall previous discussions, decisions, and context. Use when asked to search memory, check history, or before starting work to recall prior decisions.

OWASP Security provides comprehensive OWASP-aligned security guidance across six standards including Top 10 for web apps, ASVS 5.0, MASVS v2.1.0 for mobile, API Security Top 10, Kubernetes Top 10, and Agentic Applications 2026 for AI/LLM. Use for security reviews, vulnerability audits, secure implementation, Kubernetes hardening, and prompt-injection defense.

Planning creates and manages persistent markdown planning files for structured task execution. Use it when the user asks to create a plan, track progress, start a research project, or when a task requires more than 5 tool calls and needs structured phase tracking to stay focused and avoid goal drift.