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Everything your AI needs, in one place.
Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.
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A service that does a whole job for you - research, coding, support - and reports back.
Step-by-step instructions an AI follows for one kind of task. Install once, reuse everywhere.
A voice and set of rules layered onto any model: tone, audience, do's and don'ts.
A ready-to-use prompt with fill-in-the-blank variables and notes on when it works best.
A complete routing setup - models, rules and settings - in one file you can apply in a minute.
A single function an AI can call: a calculator, a search, a database lookup.
A language model endpoint with its price, speed and quality declared so the router can compare it.
videoagent-director
AI creative director that turns a user's natural-language idea into a complete storyboard and generates all assets — images, video clips, and audio — automatically. The user only describes what they w
agent-orchestrator
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
antigravity-agent-manager
Configure and orchestrate parallel agents using the standalone Antigravity 2.0 Agent Manager and Antigravity IDE.
antigravity-skill-orchestrator
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
delegating-to-agents
Delegate bounded work to other AI agents while preserving context, ownership, and progress checks.
multi-agent-architect
Design and optimize production-grade multi-agent systems with LangGraph, LangChain, and DeepAgents for complex AI workflows.
multi-agent-task-orchestrator
Route tasks to specialized AI agents with anti-duplication, quality gates, and 30-minute heartbeat monitoring
open-dynamic-workflows
Plan, orchestrate, and adversarially verify parallel AI coding agents with a dynamic multi-agent workflow engine.
orchestrate
Coordinate focused subagents on substantial work, keep their ownership non-overlapping, and integrate verified results. Use for large-scope Codex tasks; keep trivial work with the coordinator.
polis-protocol
Coordinate multi-vendor AI agents as a self-improving team — a learning router assigns work by track record and citizens can amend the protocol's own rules.
subagent-orchestrator
Coordinate quota-aware parallel subagents for large, multi-file Antigravity tasks.
task-intelligence
Protocolo de Inteligência Pré-Tarefa — ativa TODOS os agentes relevantes do ecossistema ANTES de executar qualquer tarefa solicitada pelo usuário.
ml-pipeline-automation
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
agent-engineering-expert
Build LLM agents that use tools safely: tool design, the agent loop, memory, MCP servers, multi-agent orchestration, sandboxing and prompt-injection defence. Use when the user mentions AI agents, tool
airflow-expert
Expert-level Apache Airflow orchestration, DAGs, operators, sensors, XComs, task dependencies, and scheduling. Use when the user mentions orchestration, DAGs, workflow, scheduling, or data pipeline, o
docker-expert
Expert-level Docker containerization, image optimization, and container orchestration. Use this skill for building efficient Docker images, managing containers, and implementing Docker best practices.
kubernetes-expert
Expert-level Kubernetes cluster management, deployment strategies, networking, and production operations. Use when the user mentions containers, orchestration, devops, or cloud native, or when the tas
huawei-cloud-skill-tester
End-to-end functional testing framework for Huawei Cloud skills — three-tier pipeline covering single-skill unit testing, multi-skill orchestration, and end-to-end full flow testing. Each phase produc
mpm-orchestration-demo
Reference implementation demonstrating the Command → Agent → Skill orchestration pattern in Claude MPM, showing both preloaded-skill and dynamic-skill-invocation styles
agenthub
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specializ
skill-router
This skill should be used when the user has a vague or cross-domain request and asks "which skill should I use", "is there a skill for this", "find the right skill", or "route this to the best skill"
background
Use when the user wants to see, inspect, cancel, or prune background agents fired during prior chain runs. Read/manage `.hyperflow/background/registry.json` and the per-agent output buffers at `.hyper
dispatch
Use when a task file exists in .hyperflow/tasks/ and workers need dispatching. Fans out parallel workers under per-batch Reviewers, runs a final integration review, and commits per sub-task. Endpoint
handoff
Use when managing a two-session handoff — inspecting, picking up, or reviewing a committed handoff package produced by a session=two scope run. The operator interface over the cross-environment handof
Find
Search or browse by kind. Every card shows who made it, how many people installed it and what they think.
Install
One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.
Rate and publish
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Prefer the terminal? osr stack apply registry://starter installs the starter template.