Results for “multi-agent”

Orchestrate multi-agent coding tasks via Claude DevFleet — plan projects, dispatch parallel agents in isolated worktrees, monitor progress, and read structured reports.

Orchestrate multi-agent coding tasks via Claude DevFleet — plan projects, dispatch parallel agents in isolated worktrees, monitor progress, and read structured reports.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

Spawns multiple parallel AI agents that compete on the same task using isolated git worktrees, evaluates results, and merges the best solution.

Runs a structured 6-phase multi-agent board meeting protocol for strategic decisions, with isolated C-suite contributions, critic analysis, synthesis, founder review, and decision extraction.

Designs identity, authentication, and trust verification systems for autonomous AI agents operating in multi-agent environments. Ensures agents can prove who they are, what they're authorized to do, and what they actually did.

Delegates tasks to specialized AI agents running in parallel via the Box0 platform, enabling code review, security checks, research, and documentation.

Use when facing multiple implementation tasks that could benefit from parallelization or when deciding between solo, parallel agent, sequential subagent, or agent team approaches

Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.

Inter-agent message bus with channels, queues, routing, reliability, and multiple communication patterns

[EXPLICIT INVOCATION ONLY] Creates dependency-aware implementation plans optimized for parallel multi-agent execution.

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.

Defines an inter-agent communication protocol for C-suite agent teams, including invocation syntax, loop prevention, isolation rules, and response formats to coordinate cross-functional analysis and board meetings.

Creates and manages AI agent teams for complex engineering tasks, with model routing, cost optimization, and performance evaluation.

Orchestrates three frontier models to debate a question and synthesizes their best insights into a single superior answer.

Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations,...

Call the Gemini API for text generation, chat, multimodal understanding, image/video/audio generation, streaming, function calling, structured output, and managed agents using the Interactions API in Python and TypeScript.

Expert en Ollama avancé (local LLMs, models, REST API, hardware, multi-modal, DZ deployment)

Run Claude Code autonomously in loops, from simple sequential pipelines to multi-agent DAG orchestration.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

Offload sub-tasks to other agents, specialist models, or background jobs, with guidance on choosing the right method and verifying results.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies.

Build and review high-quality technical documentation, including agent instruction files and contributor-governance files, with support for audits, multilingual parity, and sub-agent orchestration.

Create focused role prompts for agents in multi-agent topologies. Load when agent-builder needs role prompts for agents, or when a user asks to "create an agent prompt", "write a role prompt", "define agent identity", "write an agent role", "prompt for this agent", "write instructions for this agent", "agent persona". Scope: agent role prompts only (v1). System prompts, task prompts, and skill invocation prompts are future TODOs.

Content Factory multi-agent orchestrator. Use when the user says @orchestrator, asks to divide work, run a swarm, coordinate specialists, or when the task is non-trivial (multi-file, audit, cleanup, feature, incident). ALWAYS launch 5–10 agents with clear scopes; merge results; block guesswork.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.