Reference for configuring tool permissions when launching Claude Code agents. Use when setting up --allowedTools flags, restricting file access, or configuring agent permissions.
Skills（SKILL.md）は、AIエージェント（Claude Code、Cursor、Codexなど）に特定の能力を追加するための設定ファイルです。
詳しく見る →Reference for configuring tool permissions when launching Claude Code agents. Use when setting up --allowedTools flags, restricting file access, or configuring agent permissions.
Build multi-agent AI workflows with orchestration, tool use, and state management
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.
Build the `agentctl` CLI tool for AgentStack platform interaction. Implements authentication, project management, agent operations, development workflows, and evaluation commands.
AgentDB Reinforcement Learning Training operates on 3 fundamental principles:
agentdb-state-manager
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.
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.
This workflow enables you to transition from manual implementation to high-level system architecture by managing autonomous AI agents (like Devin) as "junior buddies." By shifting implementation to ag
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Architecture guidelines for Jarvy CLI - codebase structure, tool implementation patterns, registry system, platform-specific code organization, and module conventions.
Code quality guidelines for Jarvy CLI - Rust formatting, Clippy linting, error handling patterns, documentation standards, and Conventional Commits.
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.
Security best practices and guidelines for the Jarvy CLI codebase - a cross-platform development environment provisioning tool that executes system commands with elevated privileges
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.
Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.
This skill allows product managers and founders to bypass the traditional "design-to-engineering" bottleneck by acting as a "generative lead" who directs AI agents to build, deploy, and maintain softw
agentic-vision
Transform AI from a chat interface into a proactive teammate with "arms and legs." By using the Model Context Protocol (MCP) and agentic frameworks, you can move beyond "vibe coding" to autonomous exe
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.
Agent Lightning를 사용하여 AI 에이전트를 자동으로 최적화하는 방법을 제공합니다.
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.
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.
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.
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'.
Write effective AGENTS.md files for AI coding agents.
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。
Generate hierarchical AGENTS.md structures optimized for AI coding agents with minimal token usage.
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.
Generate agile release plans with sprints and roadmaps using unique sprint codes. Use when creating sprint schedules, product roadmaps, release planning, or when user mentions agile planning, sprints, roadmap, or release plans.
Plan and execute effective sprints using Agile methodologies. Define sprint goals, estimate user stories, manage sprint backlog, and facilitate daily standups to maximize team productivity and deliver value incrementally.
Orchestrate agile development workflows by invoking commands in sequence with checkpoint-based flow control. This skill should be used when the user asks to 'run the workflow', 'continue working', 'what's next', 'complete the task cycle', 'start my day', 'end the sprint', 'implement the next task', or wants guided step-by-step development assistance. Keywords: workflow, orchestrate, agile, task cycle, sprint, daily, implement, review, PR, standup, retrospective.
Agile product management, Scrum practices, and team collaboration for iterative product development.
Agno AI agent framework. Use for building multi-agent systems, AgentOS runtime, MCP server integration, and agentic AI development.
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后端代码生成 Skill，负责在 SoT 约束下生成 FastAPI 后端代码。
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前端代码生成 Skill，负责在 SoT 约束下生成 Next.js/React 前端代码。
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你是一个 **严格可控的 SoT 文档流水线执行器（Pipeline Runner）**：
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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。
Master AI agent fundamentals - architectures, ReAct patterns, cognitive loops, and autonomous system design
Better proposals, faster closes. Use when creating AI agent pricing, automation proposals, ROI calculations, or sales materials. Generates professional pricing pages, case studies, and closing scripts for AI automation services.