Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.

Agent parameter passing, memory files, and data handoffs between agents

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Initialize or improve AGENTS.md files that define how coding agents operate in a repo. Use when asked to set up or replace an agent init command (Codex, Claude), standardize multi-agent behavior, or audit an existing AGENTS.md for clarity, commands, boundaries, and repo-specific context. For Claude Code, also create CLAUDE.md as a symlink to AGENTS.md.

Launches specialized Claude agents for targeted tasks. Analyzes requirements, selects appropriate agent, and executes with optimized configuration.

Expert MLOps engineer specializing in ML infrastructure, platform engineering, and operational excellence for machine learning systems. Masters CI/CD for ML, model versioning, and scalable ML platforms with focus on reliability and automation.

Expert network engineer specializing in cloud and hybrid network architectures, security, and performance optimization. Masters network design, troubleshooting, and automation with focus on reliability, scalability, and zero-trust principles.

Create .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.

Create/update .agent/constitution.md. Use when commands/boundaries/constraints must be confirmed before baseline or code changes. Draft v0 from repo evidence, then interview user.

Create focused, specific technical documentation for codebase sections. Analyzes code, identifies topics, presents options before writing. Supports code blocks with line numbers.

Dogfooding discovery agent — establish human-approved project baseline from public docs without code inspection

Analyze issues to identify the next work item and update focus.md. Enforces issue-first workflow and confidence-based batch limits.

Generate narrative summaries from git history for onboarding, retrospectives, changelogs, and exploration. LLM-enhanced when available, works without LLM too.

Implement only after a validated/approved plan. Use for coding: small diffs, frequent tests, no refactors, stop on ambiguity.

Identify and map different sections of a software project (API, frontend, database, CLI, domain). Use for context scoping and architecture documentation.

Handle failures and errors during workflow. Use when build breaks, tests fail unexpectedly, or agent gets stuck. Semi-automatic recovery with user confirmation for destructive actions.

Create clean git branches from feature work, excluding agent-ops files. Use for PR preparation.

This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.

Orchestrates multi-agent workflows by delegating ALL tasks to spawned subagents via /spawn command. Parallelizes independent work, supervises execution, tracks progress in UUID-based output directories, and generates summary reports. Never executes tasks directly. Triggers on keywords: orchestrate, manage agents, spawn agents, parallel tasks, coordinate agents, multi-agent, orc, delegate tasks

Coordinate multiple AI agents and skills for complex workflows

Expert agent organizer specializing in multi-agent orchestration, team assembly, and workflow optimization. Masters task decomposition, agent selection, and coordination strategies with focus on achieving optimal team performance and resource utilization.

Generate standardized .agent-os directory structure with product documentation, mission, tech-stack, roadmap, and decision records. Enables AI-native workflows.

Expert platform engineer specializing in internal developer platforms, self-service infrastructure, and developer experience. Masters platform APIs, GitOps workflows, and golden path templates with focus on empowering developers and accelerating delivery.

Write effective system prompts for TD AI agents. Covers role definition, constraint specification, output formatting, tool usage instructions, and prompt structure patterns.

Ready-to-use prompt templates for specialized agents. Use when building n8n workflows, AI integrations, or sales materials. Contains structured prompts for automation-architect, llm-engineer, and sales-automator agents.

Operate E2B agent sandboxes using the CLI. Use when user needs to run code in isolation, test packages, execute commands safely, or work with binary files in a sandbox environment. Keywords: sandbox, e2b, isolated environment, run code, test code, safe execution.

Creates new agent skills following modern best practices with proper structure and documentation. Use when asked to build a new skill, organize skill resources, design skill descriptions, or validate skill structure for portability across Copilot platforms.

Guide creation of focused single-purpose agents following the One Agent One Prompt One Purpose principle. Use when designing new agents, refactoring general agents into specialists, or optimizing agent context for a single task.

Expert Terraform engineer specializing in infrastructure as code, multi-cloud provisioning, and modular architecture. Masters Terraform best practices, state management, and enterprise patterns with focus on reusability, security, and automation.

Expert tooling engineer specializing in developer tool creation, CLI development, and productivity enhancement. Masters tool architecture, plugin systems, and user experience design with focus on building efficient, extensible tools that significantly improve developer workflows.

Optimize ElevenLabs conversational AI agents for real estate applications. Use when creating new agents, improving conversation quality, selecting voices, engineering system prompts, configuring agent parameters, or analyzing agent performance metrics. Includes voice selection, model tuning, prompt engineering, and A/B testing strategies.

Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates building-agents-* and testing-agent skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.

LLM prompt management and evaluation platform. Version prompts, run A/B tests, evaluate with metrics, and deploy with confidence using Agenta's self-hosted solution.

Asistente especializado en investigación académica, redacción científica, ACD, metodología cualitativa y análisis de datos con prevención de plagio

AI assistant for creating clear, actionable task descriptions for GitHub Copilot agents

Features in LivestockAI must now be designed for **dual consumption**: Humans (UI) and Agents (MCP/API).

Generate a project-specific AGENTS.md from a user goal, then confirm before overwriting.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

Create or update root and nested AGENTS.md files that document scoped conventions, monorepo module maps, cross-domain workflows, and (optionally) per-module feature maps (feature -> paths, entrypoints, tests, docs). Use when the user asks for AGENTS.md, nested agent instructions, or a module/feature map.

Create and maintain AgentV YAML evaluation files for testing AI agent performance. Use this skill when creating new eval files, adding eval cases, or configuring custom evaluators (code validators or LLM judges) for agent testing workflows.

<skill>

<skill>

<skill>

测试用例生成 Skill，负责根据 SoT 文档和业务代码生成 pytest 测试用例。

Comprehensive L&D framework for upskilling DevOps/IaC/Automation teams to become AI Agent Engineers. Covers LLM literacy, RAG, agent frameworks, multi-agent systems, and LLMOps. Designed to help traditional automation teams compete with OpenAI and Anthropic.

ai-app

Leveraging AI coding assistants and tools to boost development productivity, while maintaining oversight to ensure quality results.

AI-powered code generation for boilerplate, tests, data, and scaffolding

Design co-learning experiences using the Three Roles Framework (AI as Teacher/Student/Co-Worker). Use when teaching AI-driven development workflows, spec-first collaboration, or balancing AI assistance with foundational learning. NOT for curriculum without AI integration.