Results for “agent-state”

Model agent mental states using BDI (Beliefs, Desires, Intentions) ontology patterns, enabling cognitive reasoning, explainability, and semantic interoperability in multi-agent systems.

Moves issues and external PRs through a state machine of triage roles, categorizing, verifying, grilling, and writing agent-ready briefs.

Display DAG state, agent progress, and branch status for an AgentHub session.

Benchmarks mobile GUI agents on multi-step tasks across 20 Android apps, measuring task completion and essential-state navigation with Task Success Rate and Essential State Achieved Rate.

This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration.

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.

`analysis-agent`/`task-agent`/`review-agent`: use for input authority, parsing, canonicalization, bounds, state checks, writable fields, rejection, or external-response changes.

Inspect and adjust the agent's runtime state, including model, context window, iteration limits, token usage, workspace configuration, subagent status, and request routing metadata.

Syncs key-value preferences and state across multiple agents using a shared TiDB Zero database, with optional auto-provisioning of a free ephemeral database.

`analysis-agent`/`task-agent`/`review-agent`: use for render sinks, browser state, server fetch, upload, redirect, cross-origin, or embedding changes; skip without web exposure.

`analysis-agent`/`task-agent`/`review-agent`: use when form fields, validation, async checks, submit states, or recovery change; skip when form behavior is unaffected.

Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. Covers supervisor, swarm, and hierarchical multi-agent patterns; subgraph composition; state management (checkpointers/stores); persistence; evals; and production debugging. Reach for this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns.

Audits external agent, skill, plugin, hook, or marketplace packs for licenses, installers, secrets, runtime state, and safe adaptation.

Launches coordinated agent teams for tasks requiring persistent cross-talk, shared state, and lifecycle management, with team design, advisor coordination, and quality gates.

Agent skill for agent - invoke with $agent-agent

Agent skill for architecture - invoke with $agent-architecture

Simulator.Company digital-twin & actor-agent specialist — talk to an agent AS an agent and delegate work to it. An agent is ANY actor whose `description` holds an "# Agent" competency profile (what it does, what it knows, whether it fits a task). The common case is a person: every workspace user has a 1:1 twin actor (`systemObjType="user"`) carrying that profile. But any actor can be an agent — a service/bot twin, a team or department, an organization, a process. This skill discovers the agent (`findAgent`), loads its profile (`getAgent`), adopts it as the persona, then either does the task, finds a better-suited agent, or hands the decision to the user (for a person: a task or a p2p message; for a non-person: propose another executor or run/trigger it as an actor). It is the actor-analog of `simulator-skills` (the `Skills`-form registry), but the registry is the workspace's agent actors. Use when the user wants to "delegate", "assign this to <someone/something>", "can <X> do this", "who/what should do this",

Add governance, safety, and trust controls to AI agent systems with policy enforcement, intent classification, and audit trails.

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.

Orchestrate teams of agents — DAG execution, routing, state sharing, failure recovery. Use when coordinating parallel agents in task pipelines with dependencies or distributing work by capability.

Quick project status report synthesized from state files, artifacts, and git activity. No agent dispatch (<2s).

Pair a remote AI agent with your browser. One command generates a setup key and prints instructions the other agent can follow to connect. Works with OpenClaw, Hermes, Codex, Cursor, or any agent that can make HTTP requests. The remote agent gets its own tab with scoped access (read+write by default, admin on request). Use when asked to "pair agent", "connect agent", "share browser", "remote browser", "let another agent use my browser", or "give browser access". (gstack) Voice triggers (speech-to-text aliases): "pair agent", "connect agent", "share my browser", "remote browser access".

Agent skill for researcher - invoke with $agent-researcher

Pair a remote AI agent with your browser. (gstack)

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

Adds input and output guardrails to an agent, including validation, content filtering, domain scoping, and rate limiting, with TypeScript examples and wiring instructions.

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.

Design multi-agent system architectures, generate tool schemas for Anthropic and OpenAI formats, and evaluate execution logs for cost, latency, and failure bottlenecks.

Discovers, installs, and manages AI agent skills from agentskill.sh, including searching, installing mid-session, scanning for security issues, and providing feedback.

Agent skill for payments - invoke with $agent-payments