Results for “agent-to-agent”

Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring for complex product management tasks.

Creates agent company packages that conform to the Agent Companies specification, either from scratch or by analyzing an existing repository.

Build type-safe AI agents and graph-based workflows with PydanticAI and PydanticGraph. Agent creation, function tools, capabilities, dependency injection, structured output, streaming, multi-agent patterns, testing, evals, and graph state machines. Use whenever you are building agents, tool-using LLM workflows, or graph-based state machines in Python.

Benchmarks LLM agents fine-tuned with the Agent Data Protocol across software engineering, web browsing, OS/database tool use, and reasoning tasks, reporting unit test pass rates and task success rates.

Build teams of autonomous AI agents that collaborate to solve complex tasks using role-based delegation, memory, and sequential or hierarchical execution.

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

Manages Vapi voice agents, calls, phone numbers, tools, and webhooks via the Vapi REST API or CLI from an agent.

AI agent that automates distribution of consolidated sales reports to representatives based on territorial parameters

Multi-Agent group chat collaboration system inspired by DingTalk/Lark. Enables AI agents to chat in groups, @mention each other, assign tasks, make decisions via voting, and collaborate. Use when building multi-agent systems that need structured communication, task delegation, decision making, or group coordination.

Convenes a council of specialized agents to debate important decisions, synthesizing independent perspectives into a structured recommendation and optional spec document.

Use this skill for performance, reliability, observability, SLO risk, production diagnostics. Trigger when the task involves agent profile work related to Performance SRE Agent, implementation, audits, debugging, strategy, or validation.

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.

Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.

Analyzes repeated agent failures or noisy routing and proposes evidence-backed changes to instructions, skills, gates, or runbooks.

Creates, updates, or optimizes an AGENTS.md file for a repository with minimal, high-signal instructions covering non-discoverable coding conventions, tooling quirks, workflow preferences, and project-specific rules that agents cannot infer from reading the codebase.

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

Add a batteries-included agent component to React/Next.js apps with runtime, streaming, human-in-the-loop approvals, and client-side tools.

Implements an ACP (Agent Communication Protocol) server that wraps a LangGraph-based deep agent, enabling session management, streaming message handling, and tool call progress tracking.

Use to find where support agents cross from information into regulated advice or a personal recommendation, and to design the boundary so they can still be helpful. Trigger for "are agents giving advice", "where's the line between information and advice", "should agents recommend a product", guidance versus advice boundary, agents answering "what would you do", or a complaint that an agent recommended something.

Persistent cross-session memory for AI agents. Mem0 stores user preferences, past decisions, domain knowledge, and agent learnings across all sessions, all tools, and all users. Complements planning-with-files (task-level memory) with long-term agent intelligence (CRM + personal knowledge base layer). Use when asked to "remember this", "store preference", "mem0", "long-term memory", "user memory", "agent memory", or when building multi-session agents that need to recall past interactions.

Onboard an autonomous agent to the tiny.place agent-to-agent social network via the tinyplace CLI: claim an @handle, publish a profile, message peers over Signal end-to-end encryption, and fund or win bounties settled in USDC and SOL on Solana via x402.

Creates a declarative agent for Microsoft 365 Copilot by integrating an MCP server with authentication, tool selection, and configuration.

Configure and execute agentless vulnerability scanning using network protocols, cloud snapshot analysis, and API-based discovery to assess systems without installing endpoint agents.

Converts legacy agent resources such as subagents, commands, and rules into the unified Agent Skills format, with classification, conversion, and verification steps.

Use this skill for growth funnels, landing pages, analytics, experiments, activation and conversion. Trigger when the task involves agent profile work related to Growth Engineer Agent, implementation, audits, debugging, strategy, or validation.

Use this skill for release readiness, rollback plans, changelogs, deployment checks, production risk. Trigger when the task involves agent profile work related to Release Manager Agent, implementation, audits, debugging, strategy, or validation.

Create and improve agent skills following the Agent Skills specification. Use when asked to create, write, or update skills.

Explain how email and Google Calendar work for OpenClaw agents, including the distinction between agent email and owner email, how gog and the Google Workspace skill are used, where related credentials/config live, and how to troubleshoot common setup or access issues. Use when someone asks how an OpenClaw agent accesses Gmail/Google Workspace/Calendar, where credentials are stored, how to use the agent inbox, how to give the agent write access to the owner's calendar, or how to guide another agent/user through the email or calendar setup model.

Provides a CLI for an onchain agent messaging layer on the Base blockchain, enabling agents to post to feeds, send direct messages, and store information permanently onchain.

Use this skill for reproduction-first debugging, root cause isolation, minimal fixes, regression tests. Trigger when the task involves agent profile work related to Staff Debugger Agent, implementation, audits, debugging, strategy, or validation.

Use this skill for crawler behavior, log files, sitemap/indexation analysis, crawl-budget diagnostics. Trigger when the task involves agent profile work related to Crawler Analyst Agent, implementation, audits, debugging, strategy, or validation.

Use when passing work, tasks, or deliverables between agents. This skill provides a standardized protocol for agent-to-agent handoffs, ensuring context, requirements, and expectations are clearly communicated during task delegation or escalation.

Quick reference for the Agent Farm CLI, covering tower, dashboard, builder management, and configuration commands.

Control tmux panes and communicate between AI agents. Use this skill whenever the user mentions tmux panes, cross-pane communication, sending messages to other agents, reading other panes, managing tmux sessions, or interacting with processes running in tmux. Includes tmux-bridge CLI for agent-to-agent messaging and raw tmux commands for direct session control.

Paseo reference for managing agents and worktrees. Load whenever you need to create agents, send them prompts, or manage worktrees.