agents-authoring
Best practices for writing AGENTS.md, agent configs, and skill files based on industry research
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Best practices for writing AGENTS.md, agent configs, and skill files based on industry research
Expert assistance for building SignalWire AI Agents in Python. Automatically activates when working with AgentBase, SWAIG functions, skills, SWML, voice configuration, DataMap, or any signalwire_agents code. Provides patterns, best practices, and complete working examples.
Set up and orchestrate multi-agent workflows using Beads (shared issue tracking/memory) and MCP Agent Mail (agent messaging). Use when coordinating multiple agents, setting up agent villages, tracking work across agent sessions, or implementing swarm workflows.
Expert guidance on building agents and tools with Pydantic AI.
Process learning resources from markdown files, extract content, identify clusters, and create Ship-Learn-Next learning paths
Add MCP servers to Claude Code configuration at user level (~/.claude). Supports stdio, HTTP, and SSE transports with environment variable prompting. Use when "add mcp server", "install mcp", "configure mcp server", "new mcp", or "setup mcp server".
Sync Azure Codex fork with OpenAI Codex upstream while preserving Azure features
Handle vision/image tasks (read, describe, analyze images) by calling Gemini CLI or Qwen Code CLI from the shell. Use for requests to interpret or describe images, extract visible text, or summarize visual content; prefer Gemini and fall back to Qwen if Gemini fails or is too generic.
Use when starting any conversation - establishes mandatory workflows for finding and using skills, including using Skill tool before announcing usage, following brainstorming before coding, and creating TodoWrite todos for checklists
Core rules for Alfred SuperAgent - 3-Layer Architecture, 4-Step Workflow, Agent-First
Set up AI inference servers on cloud GPUs. Create private LLM APIs (vLLM, TGI), image generation endpoints, embedding services, and more. All with OpenAI-compatible interfaces that work with existing tools.
Amazon Bedrock Model Customization with fine-tuning, continued pre-training, reinforcement fine-tuning (NEW 2025 - 66% accuracy gains), and distillation. Create customization jobs, monitor training, deploy custom models, and evaluate performance. Use when customizing Claude, Titan, or other Bedrock models for domain-specific tasks, adapting to proprietary data, improving accuracy on specialized workflows, or distilling large models to smaller ones.
Creates and updates modular project rules for Claude Code in .claude/rules/ directory. Use when creating, updating, or modifying rule files, organizing project guidelines, setting up code standards, or when user mentions "create rules", "update rules", "add rules", or "rule configuration".
LLM 기반 심층 리서치 스킬. 주제에 대해 자동으로 검색 계획을 수립하고, 웹 스크래핑 후 종합 리포트를 생성. 사용 시점: (1) 특정 주제에 대한 포괄적인 분석이 필요할 때 (2) 다양한 소스에서 정보를 수집하여 종합해야 할 때 (3) 마크다운 형식의 상세 리포트가 필요할 때 (4) 슬래시 커맨드 /deep-research 실행 시 Prerequisites: GEMINI_API_KEY 또는 OPENAI_API_KEY 환경 변수 필요
Integration templates for FastAPI endpoints, Next.js UI components, and Supabase schemas for ML model deployment. Use when deploying ML models, creating inference APIs, building ML prediction UIs, designing ML database schemas, integrating trained models with applications, or when user mentions FastAPI ML endpoints, prediction forms, model serving, ML API deployment, inference integration, or production ML deployment.
Use this skill when users want to generate images using OpenAI's image generation API (DALL-E or gpt-image-1), or extract text from images using OCR. Invoke when users request AI-generated images, artwork, logos, illustrations, visual content from text prompts, or need to extract text/data from images.
Patterns for building AI-powered chatbot backends using OpenAI Agents SDK with MCP (Model Context Protocol) server integration in FastAPI applications. Supports both standalone MCP servers and function tools.
Design patterns for building autonomous AI agents that can reason, plan, and execute tasks.
Integrated orchestrator agent that manages and coordinates 25 specialized AI agents for Specification Driven Development Trigger terms: orchestrate, coordinate, multi-agent, workflow, execution plan, task breakdown, agent selection, project planning, complex task, full lifecycle, end-to-end development, comprehensive solution Use when: User requests involve orchestrator tasks.