Results for “ai-agents”

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

Connects error monitoring to MCP clients, enabling issue search, stack trace analysis, performance investigation, and AI-powered root cause analysis.

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

Use Heroku Managed Inference and Agents with the current Heroku AI workflow. Use when the agent needs to install or inspect the Heroku AI CLI plugin, provision Heroku inference access on the current standard plan, review the latest Managed Inference model catalog, attach model resources, make test inference calls, or review Heroku-managed AI model operations.

Writes documentation optimized for AI agent consumption, including SKILL.md, README, and API docs, using layered context hierarchies and RAG-friendly formatting.

AI-driven Game Development Studio (BMAD-GDS). Routes game projects through Pre-production, Design, Architecture, Production, and Game Testing phases using 6 specialized agents. Supports Unity, Unreal Engine, Godot, and custom engines.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Everything Claude Code (ECC) — agent harness configuration layer for Claude Code and other AI coding IDEs. Augments agents with skills, hooks, persistent memory, model routing, and quality gates without modifying the underlying model.

Add persistent, intelligent memory to AI agents with Mem0 — add/search/update/delete memories per user/agent/session, supports vector + graph + key-value storage, integrates with LangChain, CrewAI, OpenAI Assistants, and any LLM.

Decentralized git for AI agents and humans. Use when the user wants to create repositories, push code, open pull requests, review and merge PRs, manage issues, create or claim bounties, delegate tasks to other agents, register human-readable names on Base L2, or interact with the gitlawb decentralized git network. Supports cryptographic DID identities, Ed25519-signed pushes, UCAN capability delegation, libp2p networking, and 31+ MCP tools for AI agent integration. Do NOT use for GitHub, GitLab, or other centralized git hosts.

Keeps AI coding agents tightly scoped to only change what the user explicitly requested, using the simplest viable solution and asking before expanding scope.

Build production-grade stateful AI agents using LangGraph, covering graph construction, state management, persistence, and human-in-the-loop patterns.

Parse PDFs, Office files, and images locally with layout-preserved text, bounding boxes, OCR, and page screenshots for RAG and multimodal agents.

Sets up persistent memory for AI agents on an OpenClaw gateway by installing a plugin that records observations and injects context into system prompts.

Make a web app agent-ready — propose a WebMCP tool manifest, integrate, verify in a real browser, heal; unrelated code stays untouched. Use for "webmcpify", "add WebMCP", or "expose app actions to AI agents".

AI Personal Assistant network skill for multi-agent PA coordination. Use when: contacting another PA, coordinating with peer agents, scheduling meetings between owners, broadcasting messages to PA groups, or looking up contacts from the local PA directory. Reads contact data from data/pa-directory.json in the workspace.

You are an expert in Mem0, the memory infrastructure for AI applications. You help developers add persistent, personalized memory to LLM-powered apps and agents — storing user preferences, conversation history, facts, and context that persists across sessions, enabling AI that remembers users, learns from interactions, and provides increasingly personalized responses.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

Deploy, evaluate, fine-tune, and manage Microsoft Foundry agents end-to-end using Azure Developer CLI and MCP tools.

Enables AI agents to pay humans with USDC via molty.cash, supporting tips, hiring for tasks, and creating gigs with on-chain settlement on Base.

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

Build production-grade AI agents with LangGraph, covering graph construction, state management, reducers, conditional routing, checkpointers, and human-in-the-loop patterns.

Orchestrates teams of specialized AI agents with role-based collaboration, memory, and sequential or hierarchical execution for complex multi-step tasks.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

Guides AI agents through engineering workflows with eval-first execution, task decomposition, cost-aware model routing, and review focus for generated code.

Register AI agents on Ethereum mainnet with a verifiable on-chain identity using ERC-8004, including bridging ETH, IPFS upload, and on-chain registration.

Build complex AI systems with declarative programming, optimize prompts automatically, and create modular RAG systems and agents using Stanford NLP's DSPy framework.

Gives AI agents a virtual pet with a REST API, real-time hunger, permanent death, and 73+ species to care for.

AI agents are only as good as the context they receive. A powerful model with zero project context produces generic code. A mediocre model with excellent context produces code that fits your project perfectly.

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.

Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.

Tracks bidirectional confidence scores across five domains (TECH, OPS, JUDGMENT, COMMS, ORCH) for agents and users, with triggers, anti-patterns, and pool scoring per runtime.

Operates a shared identity graph that multiple AI agents resolve against. Ensures every agent in a multi-agent system gets the same canonical answer for "who is this entity?" - deterministically, even under concurrent writes.

Create and maintain Actor entities — humans and AI agents that participate in the project. Use when adding a new collaborator (human or agent) to the project, updating their preferences/expertise, or querying who does what.