Results for “agent-workflow”

Orchestrate multi-agent workflows with task decomposition, role assignment, handoff protocols, and per-session context budgeting.

Orchestrate multi-agent workflows with CrewAI, defining role-based agents, tasks, crews, and custom tools for sequential or hierarchical collaboration.

Bootstrap and run a multi-agent AI development team with parallel chats for planning, development, QA, and DevOps.

Creates cross-platform agent skills from workflow descriptions, files, URLs, or screenshots. Supports single skills, multi-agent suites, template-based creation, cross-platform export, spec validation, and security auditing of third-party skills before install.

Create cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when "langgraph, langchain agent, stateful agent, agent graph, react agent, agent workflow, multi-step agent, langgraph, langchain, agents, state-machine, workflow, graph, ai-agents, orchestration" mentioned.

Orchestrate parallel AI agent sessions using dmux, a tmux pane manager for agent harnesses like Claude Code and Codex.

Orchestrates dynamic multi-agent workflows by planning first, then running parallel agents with adversarial verification through a local daemon or native fallback.

Guides building LLM applications on the Dify platform, covering visual workflows, knowledge bases, agents, and API deployment.

Orchestrates parallel AI agent sessions using dmux, a tmux pane manager, with patterns for research, implementation, testing, and code review across multiple harnesses.

Progressively refines context retrieval in multi-agent workflows to solve the subagent context problem.

Mines SpecStory coding histories from any agent into a persistent corpus, surfaces reproducible workflows with corroborated evidence, and interactively forges chosen ones into skills installed across agent harnesses.

Control cmux workspaces, panes, surfaces, and agent sessions safely from macOS terminal workflows.

Control cmux workspaces, panes, surfaces, and agent sessions safely from macOS terminal workflows.

Control cmux workspaces, panes, surfaces, and agent sessions safely from macOS terminal workflows.

Orchestrates multi-agent workflows defined in YAML piece files, delegating tasks to team members and managing state transitions.

Create and develop Microsoft 365 Copilot declarative agents with three workflows covering basic creation, enterprise design, and validation, using TypeSpec and the Microsoft 365 Agents Toolkit.

Generates a comprehensive AGENTS.md file at the repository root to provide AI coding agents with project context, setup commands, development workflows, testing instructions, and code style guidelines.

Build AI agents on Cloudflare Workers using the Agents SDK, covering stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, chat applications, voice agents, and browser automation.

Essential Git patterns for effective version control, eliminating redundant Git guidance per agent.

Control cmux workspaces, panes, surfaces, and agent sessions safely from macOS terminal workflows.

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

Operate inside a CodeGrid pane, drive the workspace from outside, and coordinate multi-agent workflows with local IPC.

Route AI agent engineering prompts to architecture, orchestration, evaluation, safety, debugging, context, prompt, MCP, persona, local AI, and Compound Engineering skills. Use when prompts mention agents, agent harnesses, agentic workflows, orchestration, evals, context management, MCP servers, or compound engineering.

Design n8n AI agents with best practices for node selection, sub-node wiring, tool design, structured output, and memory management.

Work incrementally on issues using Git worktrees for parallel agent workflows, starting each session by getting bearings and ending with clean state.

This skill should be used when the user asks to "/do-agent", asks for "Execute complex tasks using multi-agent architecture with context protection", or needs the workflow previously provided by the /do-agent slash command.

Execute the implementation planning workflow using the plan template to generate design artifacts.

Enforces a machine-readable behavioral contract for multi-agent development, mandating specification-first workflows, git checkpoints, context lineage, and quality gates.

Builds persistent multi-agent workflows on Claude Code using a kernel config, specialist agents, slash commands, file-based memory, and scheduled automation without external databases.

Executes DAG workflows with parallel wave processing, agent spawning, context isolation, permission enforcement, and full execution tracing. Use when running a planned DAG, managing concurrent agent execution, enforcing isolation boundaries, or tracing execution for debugging. Activate on "execute DAG", "run workflow", "spawn agents", "parallel execution", "execution trace", "agent isolation". NOT for planning DAGs (use dag-planner), validating outputs (use dag-quality), or matching skills (use dag-skills-matcher).

Embed Copilot's agentic workflows in any application using Python, TypeScript, Go, or .NET.

Connects an AI agent to Slack conversations and workspace workflows through an MCP server, with setup guidance for various environments.

Create and maintain AI-agent integration files for Pump.fun SDK work, including AGENTS/CLAUDE/COPILOT/GEMINI instructions, .well-known discovery, LLM context docs, skills registries, MCP prompts, and terminal rules. Use when wiring agents to Pump.fun development workflows.

Guides AI agents through engineering workflows with eval-first execution, task decomposition, cost-aware model routing, and review focus for generated code.

Agent profile for analyze target users, workflows, usability risks, research questions, and evidence gaps. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.