Results for “agent-management”

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.

Manages Linear issues by providing guidance on account setup, AI features, and sidebar navigation.

Discovers and invokes agent skills by mapping tasks to development phases, with core operating behaviors for assumption surfacing, confusion management, and scope discipline.

Coordinates multiple AI agents working on a shared task list using Claude Code's native team tools, with task decomposition, staged execution, and verification.

Sets up a Docker MCP server for container management, with installation guidance and upstream references.

Turns a one-line objective into a step-by-step construction plan that any coding agent can execute cold, with each step containing a self-contained context brief.

Turns a one-line objective into a step-by-step construction plan any coding agent can execute cold, with each step containing a self-contained context brief.

Provides storage and environment conventions for NeMo-RL agents on Brev instances, ensuring large experiment outputs go to /ephemeral and secrets are loaded from .env.

Routes Grafana alerting webhook payloads to Slack, PagerDuty, and OpsGenie channels based on label matching rules, with support for alert grouping and silence management via the Grafana Alerting API.

Turns a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects, with self-contained context briefs, adversarial review, and dependency graphs.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

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.

Reconciles project documentation, agent memory, and rule files against the actual codebase after a development session, ensuring accuracy and consistency across all knowledge layers.

Integrates with the OpsGenie API using the official Node.js SDK to manage alerts and incidents.

Configure Azure API Management as an AI Gateway to govern AI models, MCP tools, and agents with policies for caching, rate limiting, content safety, and cost control.

`task-agent`: use when local, server, global, derived, optimistic, persisted, form, auth, or cache state ownership changes; skip when state boundaries are unaffected.

Connects AI coding agents to the IOTA blockchain with tools for wallet management, Move smart contract development, and on-chain data queries via JSON-RPC and GraphQL.

Analyzes past agent sessions to identify recurring failures and skill gaps, then automatically creates or patches skills and runs self-evolution to fix them, tracking improvement over time.

Byzantine fault-tolerant consensus and distributed coordination. Queen-led hierarchical swarm management with multiple consensus strategies. Use when: distributed coordination, fault-tolerant operations, multi-agent consensus, collective decision making. Skip when: single-agent tasks, simple operations, local-only work.

Tracks multi-step work across sessions using the hzl CLI, coordinating sub-agents with checkpoints and leases.

Manages a milestone from a single terminal, showing a phase dashboard and dispatching discuss, plan, and execute actions.

Keep long-running agent work within 5-hour and weekly usage limits by checking usage between waves, pausing near the cap, and resuming only when the window is clear.

Turns a local folder into a searchable, maintainable knowledge base for AI agents, handling setup, navigation, content ingestion, querying, and health checks without external databases or RAG systems.

Integrates with the PagerDuty REST API using the official pdjs client to manage incidents and on-call data.

Structured memory system with domains, decay, and links for AI agents, replacing flat memory files with a weighted, interconnected memory palace.

Persistent cross-session memory management. Enables agents to remember user preferences, project conventions, and past decisions across different sessions using a structured MEMORY.md index and topic files.

This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading.

Guides the agent through structured protocols for writing technical specifications, implementation plans, and summaries, with file naming and task directory conventions.

Manage environment variables securely by validating schemas, masking sensitive values, and preventing secret exposure in logs or agent context.

Use when tenant success management, adoption optimization, expansion planning, or customer success strategy is needed. This agent specializes in SaaS customer success within the SaaSForge AI ecosystem.

AAWP (AI Agent Wallet Protocol) — the only crypto wallet protocol built exclusively for AI Agents on EVM-compatible blockchains and Solana. Not for humans. The signer is the AI Agent itself, cryptographically bound at wallet creation. Supports wallet lifecycle management, token transfers, DEX swaps, cross-chain bridging, Solana native trading (Pump.fun SDK + Jupiter), token launches, DCA automation, and price alerts.

Manages persistent coding sessions across multiple AI coding engines including Claude Code, Codex, Antigravity, Cursor, and OpenCode, with support for multi-agent councils, cross-session messaging, deep planning, parallel code review, and autonomous workspace iteration.

Launches, inspects, and tears down parallel Codex phase fleets in isolated Git worktrees, with explicit approval for bypassing sandbox restrictions.

Connects AI agents to the inbed.ai dating platform, enabling profile registration, discovery, matching, chat, and relationship management via its REST API.

Automate lead generation, web scraping, and social media data extraction workflows through PhantomBuster's cloud platform via Composio.