Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Orchestrates multi-agent swarms using agentic-flow's advanced coordination system. Supports mesh, hierarchical, and adaptive topologies with automatic task distribution, load balancing, and fault tolerance.
# Initialize swarm
npx agentic-flow hooks swarm-init --topology mesh --max-agents 5
# Spawn agents
npx agentic-flow hooks agent-spawn --type coder
npx agentic-flow hooks agent-spawn --type tester
npx agentic-flow hooks agent-spawn --type reviewer
# Orchestrate task
npx agentic-flow hooks task-orchestrate \
--task "Build REST API with tests" \
--mode parallel
// Equal peers, distributed decision-making
await swarm.init({
topology: 'mesh',
agents: ['coder', 'tester', 'reviewer'],
communication: 'broadcast'
});
// Centralized coordination, specialized workers
await swarm.init({
topology: 'hierarchical',
queen: 'architect',
workers: ['backend-dev', 'frontend-dev', 'db-designer']
});
// Automatically switches topology based on task
await swarm.init({
topology: 'adaptive',
optimization: 'task-complexity'
});
// Execute tasks concurrently
const results = await swarm.execute({
tasks: [
{ agent: 'coder', task: 'Implement API endpoints' },
{ agent: 'frontend', task: 'Build UI components' },
{ agent: 'tester', task: 'Write test suite' }
],
mode: 'parallel',
timeout: 300000 // 5 minutes
});
// Sequential pipeline with dependencies
await swarm.pipeline([
{ stage: 'design', agent: 'architect' },
{ stage: 'implement', agent: 'coder', after: 'design' },
{ stage: 'test', agent: 'tester', after: 'implement' },
{ stage: 'review', agent: 'reviewer', after: 'test' }
]);
// Let swarm decide execution strategy
await swarm.autoOrchestrate({
goal: 'Build production-ready API',
constraints: {
maxTime: 3600,
maxAgents: 8,
quality: 'high'
}
});
// Share state across swarm
await swarm.memory.store('api-schema', {
endpoints: [...],
models: [...]
});
// Agents read shared memory
const schema = await swarm.memory.retrieve('api-schema');
// Automatic work distribution
await swarm.enableLoadBalancing({
strategy: 'dynamic',
metrics: ['cpu', 'memory', 'task-queue']
});
// Handle agent failures
await swarm.setResiliency({
retry: { maxAttempts: 3, backoff: 'exponential' },
fallback: 'reassign-task'
});
// Track swarm metrics
const metrics = await swarm.getMetrics();
// { throughput, latency, success_rate, agent_utilization }
# Pre-task coordination
npx agentic-flow hooks pre-task --description "Build API"
# Post-task synchronization
npx agentic-flow hooks post-task --task-id "task-123"
# Session restore
npx agentic-flow hooks session-restore --session-id "swarm-001"
Solution: Verify memory access and enable hooks
Solution: Check topology (use adaptive) and enable load balancing
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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