Results for “agent”

Audit and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent readiness", "agent-ready score", "MCP server card", "agent skills index", "markdown for agents", "content signals", "web bot auth", "agent discovery", "RFC 9727", "RFC 8288", "RFC 9728", "SEP-1649", "WebMCP", "ARD", "ai-catalog", "x402", "UCP", "ACP", or wants to make a website discoverable and usable by AI agents.

The English summary is being prepared. Showing the original description.

Reinforces an AI agent by turning Monte Carlo's reinforcement loop diagnosis into code fixes. Reads the daily reinforcement loop report for an agent's workflows, ranks the diagnosed issues, proposes what to fix, and — with the user's approval at each step — opens a pull request. Activates on "fix my agent", "improve my agent's health", "reinforce my agent", "what should I fix in my agent". Not for investigating a specific agent alert or trace (monte-carlo-troubleshoot-agent-traces), creating agent monitors (monte-carlo-monitoring-advisor), or instrumenting a new agent (monte-carlo-instrument-agent).

The English summary is being prepared. Showing the original description.

Design an A2A Agent Card (.well-known/agent.json) manifest describing agent capabilities, skills, authentication requirements, and supported content types. Use when building an agent that must be discoverable by other A2A-compliant agents, exposing capabilities for multi-agent orchestration, migrating an existing agent to the A2A protocol, defining the public contract for an agent before implementation, or integrating with agent registries that consume Agent Cards.

The English summary is being prepared. Showing the original description.

Author and run offline evals for Agentforce agents: fixture format, scoring rubrics, hallucination checks, regression baselines, CI integration, prompt-change safety. Trigger keywords: agent eval, hallucination, doesn't hallucinate, agentforce offline evals, rubric for agent response. NOT for deciding which layers of testing the agent needs — use agentforce/agentforce-testing-strategy. NOT for Testing Center, AiEvaluationDefinition or sf agent test runs — use agentforce/agent-testing-and-evaluation.

The English summary is being prepared. Showing the original description.

Safety, privacy, cost management, and frank advice for building always-on AI agents with episodic memory. Covers data hygiene, privacy risk surfaces, runaway cost prevention, scope creep, psychological effects of persistent AI companions, and responsible deployment patterns. This is the skill that tells you what can go wrong and how to prevent it. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

The English summary is being prepared. Showing the original description.

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

The English summary is being prepared. Showing the original description.

What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and real-world examples of deployed persistent agents. Helps you go from "I have the architecture" to "here's what it actually does for me." Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).

The English summary is being prepared. Showing the original description.

Audit and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent readiness", "agent-ready score", "MCP server card", "agent skills index", "markdown for agents", "content signals", "web bot auth", "agent discovery", "RFC 9727", "RFC 8288", "RFC 9728", "SEP-1649", "WebMCP", "x402", "UCP", "ACP", or wants to make a website discoverable and usable by AI agents.

The English summary is being prepared. Showing the original description.

Seed minimal agent harness v0 — manifest, eval checks stub, governance. AUTO-INVOKED after project-setup or retroactive-project-setup when docs/harness/manifest.json is missing. Also triggers on: generate harness, scaffold agents, agent bootstrap, first time agents in this repo, new project agent setup, set up agent harness, agent onboarding files, missing agent configuration, agent instructions setup, make agents read project rules, agent reliability setup, agents not configured. Pairs with project-setup. Evolution is harness-evolution.

The English summary is being prepared. Showing the original description.

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.

The English summary is being prepared. Showing the original description.

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use jury_rig-architect), specific tool implementation.

The English summary is being prepared. Showing the original description.

Implement Agent-to-Agent (A2A) protocol for inter-agent communication. Use for agent discovery, agent cards, task delegation, and multi-agent orchestration. Triggers on "A2A protocol", "agent-to-agent", "agent discovery", "agent card", "multi-agent", "agent delegation", "agent communication", or when implementing spec/api/017-a2a-protocol.md.

The English summary is being prepared. Showing the original description.

Create and synchronize prompt-based AI agents directly within Azure AI Foundry via REST API, from a local JSON manifest. Unlike scaffolding skills that only generate local code, this skill registers agents in the Foundry service itself — making them immediately available for invocation. Use when the user asks to create agents in Foundry, sync, deploy, register, or push agents to Foundry, update agent instructions, or scaffold the manifest and sync script for a new repository. Triggers: 'create agent in foundry', 'sync foundry agents', 'deploy agents to foundry', 'register agents in foundry', 'push agents', 'create foundry agent manifest', 'scaffold agent sync'.

The English summary is being prepared. Showing the original description.

Set up and operate Hermes Agent across terminal, desktop, and messaging interfaces. This guide covers model configuration, themes, extensions, and coordination of independent agent instances, with references for task-specific settings and troubleshooting.

• Set up Hermes Agent and choose a model
• Connect messaging services
• Customize themes and interface extensions

Create an AI agent company or team package from an idea or an existing repository. Define roles, reporting lines, skills, and work handoffs, then generate structured files that follow the Agent Companies specification.

• Build an agent company from an idea
• Turn a repository into an agent team package
• Define agent roles and work handoffs

Refactor claude-flow into an extension of agentic-flow@alpha to reduce duplicate implementations. Plan adapter layers, migrate agent and task management, and connect learning, shared memory, and tools while checking compatibility with existing functionality.

• Consolidate duplicate agent and task management code
• Design an adapter that preserves existing APIs
• Integrate learning, shared memory, and MCP tools

Organize complex work across specialized agents with claude-flow. Start and stop agents, check their status and metrics, and manage groups by role, using shared memory to coordinate tasks such as development, reviews, and security checks.

• Assign bug fixes across research, coding, and testing agents
• Coordinate agents for feature development and review
• Organize specialists for security checks

Give an AI agent an email address through the AgentMail API for theagentmail.net. Covers account creation, sending and reading messages, retrieving attachments, setting up incoming-email notifications, and checking organization billing balances.

• Creating an email account for an AI agent
• Retrieving verification emails for authorized service signups
• Sending authorized messages and checking replies

Scan a codebase to create project documentation and project-specific agent instructions. The first run examines the whole project; later runs focus on changed files, with human confirmation required before updating agent instructions after architectural changes.

• Create documentation when starting agent-assisted development
• Refresh project documentation after architecture changes
• Check changed code for documentation drift

Plan Claude Code agent teams by choosing team size, roles, and agent types with the tools each task needs. Preset compositions cover reviews, debugging, implementation, research, security audits, and migrations, alongside display settings for local or CI environments.

• Choose team size and roles for a development task
• Assign separate review areas to multiple agents
• Split bug investigation across competing hypotheses

Build and operate AutoGPT agents that carry out multi-step workflows. Covers a visual editor for connecting processing blocks, scheduled and webhook triggers, deployment, execution monitoring, and benchmarks for evaluating agent performance.

• Build an AI workflow in a visual editor
• Run agents on a schedule or external event
• Add custom agent abilities with Forge

Design AI agents that plan tasks, call tools, and retain useful information. Covers coordination between agents, evaluation, and debugging, with practical patterns for execution limits, error handling, and tracing what an agent does.

• Designing an agent that plans and executes tasks
• Organizing tool definitions and selection
• Deciding what an agent should remember

Design and generate agents in Nanobot by finding reusable tools, skills, prompts, and existing agent configurations. Guide requirements and confirmation, create focused agent assets, and follow recorded real-data testing and evaluation before publication.

• Designing an agent around existing project resources
• Adapting existing agent configurations and prompts
• Identifying missing tools and calculation capabilities

Drives a CARLA vehicle to a specific destination using navigation agents that plan routes, avoid obstacles, and obey traffic lights. Can also plan and draw a route between two points without moving the vehicle.

• Driving a vehicle to specified coordinates
• Navigating to a chosen junction
• Planning and drawing a route without driving