agenthero-ai
AgentHero AI - Hierarchical multi-agent orchestration system with PM coordination, file-based state management, and interactive menu interface. Use when managing complex multi-agent workflows, coordinating parallel sub-agents, or organizing large project tasks with multiple specialists. All created agents use aghero- prefix.
agentic_architecture
Enforces high-level architectural thinking, separation of concerns, and scalability checks before coding.
agentic-chat
AI assistant for creating clear, actionable task descriptions for GitHub Copilot agents
agentic-coach
Interactive prompt engineering coach that elevates vague prompts through Socratic dialogue, multiple transformation styles, and guided learning. Use when improving prompts, learning agentic engineering, or wanting coached guidance rather than automated transformation. NEVER auto-executes - always displays and asks first.
agentic-design
Use when building AI agent systems. Covers agent loops, tool calling, planning patterns, memory systems, multi-agent coordination, and safety guardrails. Apply when creating autonomous AI workflows, coding assistants, or task automation systems.
agentic-development
Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
agentic-diffusion
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agentic-docs
Write clear, plain-spoken code comments and documentation that lives alongside the code. Use when writing or reviewing code that needs inline documentation like file headers, function docs, architectural decisions, or explanatory comments. Works well for both human readers and AI coding assistants who see one file at a time.
agentic-engineering-workflow
Transition from a hands-on "bricklayer" to a high-level "architect" by managing a fleet of autonomous AI agents. Use this when you need to scale engineering output with a small team, handle repetitive migrations/bug fixes, or onboard engineers to complex legacy codebases.
agentic-eval
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Agentic Feature Design
Designing features for the "Action Era" that are AI-accessible by default
agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
agentic-jumpstart-architecture
Architecture guidelines for Jarvy CLI - codebase structure, tool implementation patterns, registry system, platform-specific code organization, and module conventions.
agentic-jumpstart-code-quality
Code quality guidelines for Jarvy CLI - Rust formatting, Clippy linting, error handling patterns, documentation standards, and Conventional Commits.
agentic-jumpstart-dependency-management
Dependency management guidelines for Jarvy - crate selection criteria, feature flag best practices, version management, security auditing with cargo-audit and cargo-deny.
agentic-jumpstart-performance
Performance optimization guidelines for Rust CLI tools. Covers efficient command execution, parallel processing, lazy initialization, allocation minimization, config parsing, and build optimizations for cross-platform CLI applications.
agentic-jumpstart-security
Security best practices and guidelines for the Jarvy CLI codebase - a cross-platform development environment provisioning tool that executes system commands with elevated privileges
agentic-jumpstart-testing
Testing guidelines for Jarvy CLI - unit testing patterns, integration tests with assert_cmd, test environment variables, platform-specific testing, and CI coverage strategies.
agentic-kpi-tracking
Track and measure agentic coding KPIs for ZTE progression. Use when measuring workflow effectiveness, tracking Size/Attempts/Streak/Presence metrics, or assessing readiness for autonomous operation.
agentic-layer-assessment
Assess agentic layer maturity using the 12-grade classification system (Class 1-3). Use when evaluating codebase readiness, identifying next upgrade steps, or tracking progress toward the Codebase Singularity.
agentic-layer-audit
Audit codebase for agentic layer coverage and identify gaps. Use when assessing agentic layer maturity, identifying investment opportunities, or evaluating primitive coverage.
agentic-orchestrating
Provides the "how-to" for workflow / task execution orchestration. Defines methods for planning multi-phase task/workflows, implementing them through agent delegation, and managing artifacts.
agentic-orchestration
Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.
agentic-patterns
Design and operate multi-agent orchestration patterns (ReAct loops, evaluator-optimizer, orchestrator-workers, tool routing) for LLM systems. Use when building or debugging agent workflows, tool-use loops, or multi-step task delegation; triggers: agentic, multi-agent, orchestration, ReAct, evaluator-optimizer, tool-use, handoff.
agentic-product-prototyping
Build functional software prototypes and MVPs without deep technical skills by using AI agents as "developers in your pocket." Use this skill when you need to validate a new product concept, build custom back-office tools (e.g., a real estate data manager), or create high-fidelity demos to unblock engineering roadmaps.
agentic-quality-engineering
AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles.
agentic-structure
Collaborative programming framework for production-ready development. Use when starting features, writing code, handling security/errors, adding comments, discussing requirements, or encountering knowledge gaps. Applies to all development tasks for clear, safe, maintainable code.
agentic-vision
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agentic-workflow-automation
Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.
agentic-workflow-guide
Design, review, and improve agent workflows & agent using SSOT, SRP, Fail Fast principles. Supports Prompt Chaining, Parallelization, Orchestrator-Workers patterns.
agentic-workflows
Design and implement agentic AI workflows and patterns. Covers ReAct, planning agents, tool use, memory systems, and multi-agent orchestration. Use when building autonomous AI agents, implementing complex task automation, or designing intelligent workflow systems.
agentica-claude-proxy
Guide for integrating Agentica SDK with Claude Code CLI proxy
agentica-sdk
Build Python agents with Agentica SDK - @agentic decorator, spawn(), persistence, MCP integration
agentica-server
Agentica server + Claude proxy setup - architecture, startup sequence, debugging
agentica-spawn
Spawn Agentica multi-agent patterns
agenticflow-skills
Comprehensive guide for building AI workflows, agents, and workforce systems with AgenticFlow. Use when designing workflows with various node types, configuring single agents, or orchestrating workforce collaboration patterns.
agentkit
Coinbase AgentKit - Toolkit for enabling AI agents with crypto wallets and onchain capabilities. Use for building autonomous agents that can execute transfers, swaps, DeFi operations, NFT minting, smart contract deployment, and gasless transactions via Smart Wallets.
agentlightning-skill
Agent Lightning를 사용하여 AI 에이전트를 자동으로 최적화하는 방법을 제공합니다.
agentmail
Inter-agent communication for tmux sessions. Send and receive messages between AI agents.
agentmd-creator
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autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
agents-bootstrap
Generate a project-specific AGENTS.md from a user goal, then confirm before overwriting.
agents
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
llamaindex
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.
agents-manager
Branch skill for building and improving agents. Use when creating new agents, adapting marketplace agents, validating agent structure, writing system prompts, or improving existing agents. Triggers: 'create agent', 'improve agent', 'validate agent', 'fix agent', 'agent frontmatter', 'system prompt', 'adapt agent', 'customize agent', 'agent examples', 'agent tools'.
agents-md-authoring-majo
Write effective AGENTS.md files for AI coding agents.
agents-md-creator
AIコーディングエージェント向けの指示書「AGENTS.md」を作成するスキル。プロジェクトにAIエージェントが作業するための文脈と指示を集約するファイルを作成したい場合に使用します。「AGENTS.mdを作成」「AIエージェント用の指示書を作る」「エージェント向けREADMEを作成」などのリクエストでトリガーします。OpenAI Codex、Claude Code、GitHub Copilot、Cursorなど、複数のAIエージェントで共通利用できるオープンな標準フォーマットです。
agentuity-cli-auth-ssh-list
List all SSH keys on your account. Requires authentication. Use for managing authentication credentials
agents-md-generator
Generate hierarchical AGENTS.md structures for codebases. Use when user asks to create AGENTS.md files, analyze codebase for AI agent documentation, set up AI-friendly project documentation, or generate context files for AI coding assistants. Triggers on "create AGENTS.md", "generate agents", "analyze codebase for AI", "AI documentation setup", "hierarchical agents".
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