Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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
まだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。