Skip to content
cccskills
FreePublished on GitHubEnglish overview

V3 MCP Optimization

Improve the MCP server that connects claude-flow v3 to tools. It guides connection reuse, faster tool lookup, request distribution, and communication changes, with monitoring to evaluate response times, errors, memory use, and cache performance.

OriginalShow the original description

MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.

AI rating70/ 100See how to install

Good for

  • Analyze slow MCP startup and tool responses
  • Reuse connections and clean up idle ones
  • Improve tool lookup and distribute requests
  • Monitor latency, errors, and cache performance

Overview

What it does

Investigate startup and tool-execution delays in the MCP server for claude-flow v3, and get support implementing improvements. MCP connects AI systems with external tools. The skill covers managing reusable connections, indexing tools for fast lookup by name, distributing requests across multiple servers, and batching or compressing communications.

It also includes implementation examples for measuring response times, error rates, memory use, and cache activity. These measurements help assess the changes made to the server.

When it helps

Use it when tool lookup slows as the number of tools grows, or when creating a connection for every request adds overhead. It is intended for developers who want to use measurements to identify where to improve performance, then implement the changes and monitoring together. Connection reuse, idle-connection cleanup, and connection health monitoring are among the tasks it supports.

Example requests

  • “Analyze MCP server performance and identify the causes of delays.”
  • “Implement connection reuse and connection health monitoring.”
  • “Improve tool lookup and load balancing, and record response times.”

Notes

This skill targets the claude-flow v3 MCP server implementation. Code examples use TypeScript and @modelcontextprotocol/sdk. Numbers such as a 95th-percentile response time below 100 ms are targets, not guarantees of performance after installation.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt identifies MCP latency sources and improvement areas such as connection pools and tool indexes.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
BPartly lackingIt provides connection-pool code and target metrics, but referenced helper implementations are absent.
SafetyAre sending data, deleting files, and running commands disclosed?
BPartly lackingIt handles creating and closing connections but provides no procedure for checking effects on active connections.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingIt uses claude-flow v3 and the SDK but provides no installation steps or runtime requirements.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 21, 2026. Treat it as a guide and read the original before installing.

Disagree? Request a re-evaluation

Tell us which point is wrong and why. No login needed. We will check it against the SKILL.md, redo the evaluation, and show the result here.

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

Files included (1)

  • SKILL.md20.9 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

V3 MCP Optimization

What This Skill Does

Optimizes claude-flow v3 MCP (Model Context Protocol) server implementation with advanced transport layer optimizations, connection pooling, load balancing, and comprehensive performance monitoring to achieve sub-100ms response times.

Quick Start

# Initialize MCP optimization analysis
Task("MCP architecture", "Analyze current MCP server performance and bottlenecks", "mcp-specialist")

# Optimization implementation (parallel)
Task("Connection pooling", "Implement MCP connection pooling and reuse", "mcp-specialist")
Task("Load balancing", "Add dynamic load balancing for MCP tools", "mcp-specialist")
Task("Transport optimization", "Optimize transport layer performance", "mcp-specialist")

MCP Performance Architecture

Current State Analysis

Current MCP Issues:
├── Cold Start Latency: ~1.8s MCP server init
├── Connection Overhead: New connection per request
├── Tool Registry: Linear search O(n) for 213+ tools
├── Transport Layer: No connection reuse
└── Memory Usage: No cleanup of idle connections

Target Performance:
├── Startup Time: <400ms (4.5x improvement)
├── Tool Lookup: <5ms (O(1) hash table)
├── Connection Reuse: 90%+ connection pool hits
├── Response Time: <100ms p95
└── Memory Efficiency: 50% reduction

MCP Server Architecture

// src/core/mcp/mcp-server.ts
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';

interface OptimizedMCPConfig {
  // Connection pooling
  maxConnections: number;
  idleTimeoutMs: number;
  connectionReuseEnabled: boolean;

  // Tool registry
  toolCacheEnabled: boolean;
  toolIndexType: 'hash' | 'trie';

  // Performance
  requestTimeoutMs: number;
  batchingEnabled: boolean;
  compressionEnabled: boolean;

  // Monitoring
  metricsEnabled: boolean;
  healthCheckIntervalMs: number;
}

export class OptimizedMCPServer {
  private server: Server;
  private connectionPool: ConnectionPool;
  private toolRegistry: FastToolRegistry;
  private loadBalancer: MCPLoadBalancer;
  private metrics: MCPMetrics;

  constructor(config: OptimizedMCPConfig) {
    this.server = new Server({
      name: 'claude-flow-v3',
      version: '3.0.0'
    }, {
      capabilities: {
        tools: { listChanged: true },
        resources: { subscribe: true, listChanged: true },
        prompts: { listChanged: true }
      }
    });

    this.connectionPool = new ConnectionPool(config);
    this.toolRegistry = new FastToolRegistry(config.toolIndexType);
    this.loadBalancer = new MCPLoadBalancer();
    this.metrics = new MCPMetrics(config.metricsEnabled);
  }

  async start(): Promise<void> {
    // Pre-warm connection pool
    await this.connectionPool.preWarm();

    // Pre-build tool index
    await this.toolRegistry.buildIndex();

    // Setup request handlers with optimizations
    this.setupOptimizedHandlers();

    // Start health monitoring
    this.startHealthMonitoring();

    // Start server
    const transport = new StdioServerTransport();
    await this.server.connect(transport);

    this.metrics.recordStartup();
  }
}

Connection Pool Implementation

Advanced Connection Pooling

// src/core/mcp/connection-pool.ts
interface PooledConnection {
  id: string;
  connection: MCPConnection;
  lastUsed: number;
  usageCount: number;
  isHealthy: boolean;
}

export class ConnectionPool {
  private pool: Map<string, PooledConnection> = new Map();
  private readonly config: ConnectionPoolConfig;
  private healthChecker: HealthChecker;

  constructor(config: ConnectionPoolConfig) {
    this.config = {
      maxConnections: 50,
      minConnections: 5,
      idleTimeoutMs: 300000, // 5 minutes
      maxUsageCount: 1000,
      healthCheckIntervalMs: 30000,
      ...config
    };

    this.healthChecker = new HealthChecker(this.config.healthCheckIntervalMs);
  }

  async getConnection(endpoint: string): Promise<MCPConnection> {
    const start = performance.now();

    // Try to get from pool first
    const pooled = this.findAvailableConnection(endpoint);
    if (pooled) {
      pooled.lastUsed = Date.now();
      pooled.usageCount++;

      this.recordMetric('pool_hit', performance.now() - start);
      return pooled.connection;
    }

    // Check pool capacity
    if (this.pool.size >= this.config.maxConnections) {
      await this.evictLeastUsedConnection();
    }

    // Create new connection
    const connection = await this.createConnection(endpoint);
    const pooledConn: PooledConnection = {
      id: this.generateConnectionId(),
      connection,
      lastUsed: Date.now(),
      usageCount: 1,
      isHealthy: true
    };

    this.pool.set(pooledConn.id, pooledConn);
    this.recordMetric('pool_miss', performance.now() - start);

    return connection;
  }

  async releaseConnection(connection: MCPConnection): Promise<void> {
    // Mark connection as available for reuse
    const pooled = this.findConnectionById(connection.id);
    if (pooled) {
      // Check if connection should be retired
      if (pooled.usageCount >= this.config.maxUsageCount) {
        await this.removeConnection(pooled.id);
      }
    }
  }

  async preWarm(): Promise<void> {
    const connections: Promise<MCPConnection>[] = [];

    for (let i = 0; i < this.config.minConnections; i++) {
      connections.push(this.createConnection('default'));
    }

    await Promise.all(connections);
  }

  private async evictLeastUsedConnection(): Promise<void> {
    let oldestConn: PooledConnection | null = null;
    let oldestTime = Date.now();

    for (const conn of this.pool.values()) {
      if (conn.lastUsed < oldestTime) {
        oldestTime = conn.lastUsed;
        oldestConn = conn;
      }
    }

    if (oldestConn) {
      await this.removeConnection(oldestConn.id);
    }
  }

  private findAvailableConnection(endpoint: string): PooledConnection | null {
    for (const conn of this.pool.values()) {
      if (conn.isHealthy &&
          conn.connection.endpoint === endpoint &&
          Date.now() - conn.lastUsed < this.config.idleTimeoutMs) {
        return conn;
      }
    }
    return null;
  }
}

Fast Tool Registry

O(1) Tool Lookup Implementation

// src/core/mcp/fast-tool-registry.ts
interface ToolIndexEntry {
  name: string;
  handler: ToolHandler;
  metadata: ToolMetadata;
  usageCount: number;
  avgLatencyMs: number;
}

export class FastToolRegistry {
  private toolIndex: Map<string, ToolIndexEntry> = new Map();
  private categoryIndex: Map<string, string[]> = new Map();
  private fuzzyMatcher: FuzzyMatcher;
  private cache: LRUCache<string, ToolIndexEntry>;

  constructor(indexType: 'hash' | 'trie' = 'hash') {
    this.fuzzyMatcher = new FuzzyMatcher();
    this.cache = new LRUCache<string, ToolIndexEntry>(1000); // Cache 1000 most used tools
  }

  async buildIndex(): Promise<void> {
    const start = performance.now();

    // Load all available tools
    const tools = await this.loadAllTools();

    // Build hash index for O(1) lookup
    for (const tool of tools) {
      const entry: ToolIndexEntry = {
        name: tool.name,
        handler: tool.handler,
        metadata: tool.metadata,
        usageCount: 0,
        avgLatencyMs: 0
      };

      this.toolIndex.set(tool.name, entry);

      // Build category index
      const category = tool.metadata.category || 'general';
      if (!this.categoryIndex.has(category)) {
        this.categoryIndex.set(category, []);
      }
      this.categoryIndex.get(category)!.push(tool.name);
    }

    // Build fuzzy search index
    await this.fuzzyMatcher.buildIndex(tools.map(t => t.name));

    console.log(`Tool index built in ${(performance.now() - start).toFixed(2)}ms for ${tools.length} tools`);
  }

  findTool(name: string): ToolIndexEntry | null {
    // Try cache first
    const cached = this.cache.get(name);
    if (cached) return cached;

    // Try exact match
    const exact = this.toolIndex.get(name);
    if (exact) {
      this.cache.set(name, exact);
      return exact;
    }

    // Try fuzzy match
    const fuzzyMatches = this.fuzzyMatcher.search(name, 1);
    if (fuzzyMatches.length > 0) {
      const match = this.toolIndex.get(fuzzyMatches[0]);
      if (match) {
        this.cache.set(name, match);
        return match;
      }
    }

    return null;
  }

  findToolsByCategory(category: string): ToolIndexEntry[] {
    const toolNames = this.categoryIndex.get(category) || [];
    return toolNames
      .map(name => this.toolIndex.get(name))
      .filter(entry => entry !== undefined) as ToolIndexEntry[];
  }

  getMostUsedTools(limit: number = 10): ToolIndexEntry[] {
    return Array.from(this.toolIndex.values())
      .sort((a, b) => b.usageCount - a.usageCount)
      .slice(0, limit);
  }

  recordToolUsage(toolName: string, latencyMs: number): void {
    const entry = this.toolIndex.get(toolName);
    if (entry) {
      entry.usageCount++;
      // Moving average for latency
      entry.avgLatencyMs = (entry.avgLatencyMs + latencyMs) / 2;
    }
  }
}

Load Balancing & Request Distribution

Intelligent Load Balancer

// src/core/mcp/load-balancer.ts
interface ServerInstance {
  id: string;
  endpoint: string;
  load: number;
  responseTime: number;
  isHealthy: boolean;
  maxConnections: number;
  currentConnections: number;
}

export class MCPLoadBalancer {
  private servers: Map<string, ServerInstance> = new Map();
  private routingStrategy: RoutingStrategy = 'least-connections';

  addServer(server: ServerInstance): void {
    this.servers.set(server.id, server);
  }

  selectServer(toolCategory?: string): ServerInstance | null {
    const healthyServers = Array.from(this.servers.values())
      .filter(server => server.isHealthy);

    if (healthyServers.length === 0) return null;

    switch (this.routingStrategy) {
      case 'round-robin':
        return this.roundRobinSelection(healthyServers);

      case 'least-connections':
        return this.leastConnectionsSelection(healthyServers);

      case 'response-time':
        return this.responseTimeSelection(healthyServers);

      case 'weighted':
        return this.weightedSelection(healthyServers, toolCategory);

      default:
        return healthyServers[0];
    }
  }

  private leastConnectionsSelection(servers: ServerInstance[]): ServerInstance {
    return servers.reduce((least, current) =>
      current.currentConnections < least.currentConnections ? current : least
    );
  }

  private responseTimeSelection(servers: ServerInstance[]): ServerInstance {
    return servers.reduce((fastest, current) =>
      current.responseTime < fastest.responseTime ? current : fastest
    );
  }

  private weightedSelection(servers: ServerInstance[], category?: string): ServerInstance {
    // Prefer servers with lower load and better response time
    const scored = servers.map(server => ({
      server,
      score: this.calculateServerScore(server, category)
    }));

    scored.sort((a, b) => b.score - a.score);
    return scored[0].server;
  }

  private calculateServerScore(server: ServerInstance, category?: string): number {
    const loadFactor = 1 - (server.currentConnections / server.maxConnections);
    const responseFactor = 1 / (server.responseTime + 1);
    const categoryBonus = this.getCategoryBonus(server, category);

    return loadFactor * 0.4 + responseFactor * 0.4 + categoryBonus * 0.2;
  }

  updateServerMetrics(serverId: string, metrics: Partial<ServerInstance>): void {
    const server = this.servers.get(serverId);
    if (server) {
      Object.assign(server, metrics);
    }
  }
}

Transport Layer Optimization

High-Performance Transport

// src/core/mcp/optimized-transport.ts
export class OptimizedTransport {
  private compression: boolean = true;
  private batching: boolean = true;
  private batchBuffer: MCPMessage[] = [];
  private batchTimeout: NodeJS.Timeout | null = null;

  constructor(private config: TransportConfig) {}

  async send(message: MCPMessage): Promise<void> {
    if (this.batching && this.canBatch(message)) {
      this.addToBatch(message);
      return;
    }

    await this.sendImmediate(message);
  }

  private async sendImmediate(message: MCPMessage): Promise<void> {
    const start = performance.now();

    // Compress if enabled
    const payload = this.compression
      ? await this.compress(message)
      : message;

    // Send through transport
    await this.transport.send(payload);

    // Record metrics
    this.recordLatency(performance.now() - start);
  }

  private addToBatch(message: MCPMessage): void {
    this.batchBuffer.push(message);

    // Start batch timeout if not already running
    if (!this.batchTimeout) {
      this.batchTimeout = setTimeout(
        () => this.flushBatch(),
        this.config.batchTimeoutMs || 10
      );
    }

    // Flush if batch is full
    if (this.batchBuffer.length >= this.config.maxBatchSize) {
      this.flushBatch();
    }
  }

  private async flushBatch(): Promise<void> {
    if (this.batchBuffer.length === 0) return;

    const batch = this.batchBuffer.splice(0);
    this.batchTimeout = null;

    // Send as single batched message
    await this.sendImmediate({
      type: 'batch',
      messages: batch
    });
  }

  private canBatch(message: MCPMessage): boolean {
    // Don't batch urgent messages or responses
    return message.type !== 'response' &&
           message.priority !== 'high' &&
           message.type !== 'error';
  }

  private async compress(data: any): Promise<Buffer> {
    // Use fast compression for smaller messages
    return gzipSync(JSON.stringify(data));
  }
}

Performance Monitoring

Real-time MCP Metrics

// src/core/mcp/metrics.ts
interface MCPMetrics {
  requestCount: number;
  errorCount: number;
  avgResponseTime: number;
  p95ResponseTime: number;
  connectionPoolHits: number;
  connectionPoolMisses: number;
  toolLookupTime: number;
  startupTime: number;
}

export class MCPMetricsCollector {
  private metrics: MCPMetrics;
  private responseTimeBuffer: number[] = [];
  private readonly bufferSize = 1000;

  constructor() {
    this.metrics = this.createInitialMetrics();
  }

  recordRequest(latencyMs: number): void {
    this.metrics.requestCount++;
    this.updateResponseTimes(latencyMs);
  }

  recordError(): void {
    this.metrics.errorCount++;
  }

  recordConnectionPoolHit(): void {
    this.metrics.connectionPoolHits++;
  }

  recordConnectionPoolMiss(): void {
    this.metrics.connectionPoolMisses++;
  }

  recordToolLookup(latencyMs: number): void {
    this.metrics.toolLookupTime = this.updateMovingAverage(
      this.metrics.toolLookupTime,
      latencyMs
    );
  }

  recordStartup(latencyMs: number): void {
    this.metrics.startupTime = latencyMs;
  }

  getMetrics(): MCPMetrics {
    return { ...this.metrics };
  }

  getHealthStatus(): HealthStatus {
    const errorRate = this.metrics.errorCount / this.metrics.requestCount;
    const poolHitRate = this.metrics.connectionPoolHits /
      (this.metrics.connectionPoolHits + this.metrics.connectionPoolMisses);

    return {
      status: this.determineHealthStatus(errorRate, poolHitRate),
      errorRate,
      poolHitRate,
      avgResponseTime: this.metrics.avgResponseTime,
      p95ResponseTime: this.metrics.p95ResponseTime
    };
  }

  private updateResponseTimes(latency: number): void {
    this.responseTimeBuffer.push(latency);

    if (this.responseTimeBuffer.length > this.bufferSize) {
      this.responseTimeBuffer.shift();
    }

    this.metrics.avgResponseTime = this.calculateAverage(this.responseTimeBuffer);
    this.metrics.p95ResponseTime = this.calculatePercentile(this.responseTimeBuffer, 95);
  }

  private calculatePercentile(arr: number[], percentile: number): number {
    const sorted = arr.slice().sort((a, b) => a - b);
    const index = Math.ceil((percentile / 100) * sorted.length) - 1;
    return sorted[index] || 0;
  }

  private determineHealthStatus(errorRate: number, poolHitRate: number): 'healthy' | 'warning' | 'critical' {
    if (errorRate > 0.1 || poolHitRate < 0.5) return 'critical';
    if (errorRate > 0.05 || poolHitRate < 0.7) return 'warning';
    return 'healthy';
  }
}

Tool Registry Optimization

Pre-compiled Tool Index

// src/core/mcp/tool-precompiler.ts
export class ToolPrecompiler {
  async precompileTools(): Promise<CompiledToolRegistry> {
    const tools = await this.loadAllTools();

    // Create optimized lookup structures
    const nameIndex = new Map<string, Tool>();
    const categoryIndex = new Map<string, Tool[]>();
    const fuzzyIndex = new Map<string, string[]>();

    for (const tool of tools) {
      // Exact name index
      nameIndex.set(tool.name, tool);

      // Category index
      const category = tool.metadata.category || 'general';
      if (!categoryIndex.has(category)) {
        categoryIndex.set(category, []);
      }
      categoryIndex.get(category)!.push(tool);

      // Pre-compute fuzzy variations
      const variations = this.generateFuzzyVariations(tool.name);
      for (const variation of variations) {
        if (!fuzzyIndex.has(variation)) {
          fuzzyIndex.set(variation, []);
        }
        fuzzyIndex.get(variation)!.push(tool.name);
      }
    }

    return {
      nameIndex,
      categoryIndex,
      fuzzyIndex,
      totalTools: tools.length,
      compiledAt: new Date()
    };
  }

  private generateFuzzyVariations(name: string): string[] {
    const variations: string[] = [];

    // Common typos and abbreviations
    variations.push(name.toLowerCase());
    variations.push(name.replace(/[-_]/g, ''));
    variations.push(name.replace(/[aeiou]/gi, '')); // Consonants only

    // Add more fuzzy matching logic as needed

    return variations;
  }
}

Advanced Caching Strategy

Multi-Level Caching

// src/core/mcp/multi-level-cache.ts
export class MultiLevelCache {
  private l1Cache: Map<string, any> = new Map(); // In-memory, fastest
  private l2Cache: LRUCache<string, any>; // LRU cache, larger capacity
  private l3Cache: DiskCache; // Persistent disk cache

  constructor(config: CacheConfig) {
    this.l2Cache = new LRUCache<string, any>({
      max: config.l2MaxEntries || 10000,
      ttl: config.l2TTL || 300000 // 5 minutes
    });

    this.l3Cache = new DiskCache(config.l3Path || './.cache/mcp');
  }

  async get(key: string): Promise<any | null> {
    // Try L1 cache first (fastest)
    if (this.l1Cache.has(key)) {
      return this.l1Cache.get(key);
    }

    // Try L2 cache
    const l2Value = this.l2Cache.get(key);
    if (l2Value) {
      // Promote to L1
      this.l1Cache.set(key, l2Value);
      return l2Value;
    }

    // Try L3 cache (disk)
    const l3Value = await this.l3Cache.get(key);
    if (l3Value) {
      // Promote to L2 and L1
      this.l2Cache.set(key, l3Value);
      this.l1Cache.set(key, l3Value);
      return l3Value;
    }

    return null;
  }

  async set(key: string, value: any, options?: CacheOptions): Promise<void> {
    // Set in all levels
    this.l1Cache.set(key, value);
    this.l2Cache.set(key, value);

    if (options?.persistent) {
      await this.l3Cache.set(key, value);
    }

    // Manage L1 cache size
    if (this.l1Cache.size > 1000) {
      const firstKey = this.l1Cache.keys().next().value;
      this.l1Cache.delete(firstKey);
    }
  }
}

Success Metrics

Performance Targets

  • Startup Time: <400ms MCP server initialization (4.5x improvement)
  • Response Time: <100ms p95 for tool execution
  • Tool Lookup: <5ms average lookup time
  • Connection Pool: >90% hit rate
  • Memory Usage: 50% reduction in idle memory
  • Error Rate: <1% failed requests
  • Throughput: >1000 requests/second

Monitoring Dashboards

const mcpDashboard = {
  metrics: [
    'Request latency (p50, p95, p99)',
    'Error rate by tool category',
    'Connection pool utilization',
    'Tool lookup performance',
    'Memory usage trends',
    'Cache hit rates (L1, L2, L3)'
  ],

  alerts: [
    'Response time >200ms for 5 minutes',
    'Error rate >5% for 1 minute',
    'Pool hit rate <70% for 10 minutes',
    'Memory usage >500MB for 5 minutes'
  ]
};

Related V3 Skills

  • v3-core-implementation - Core domain integration with MCP
  • v3-performance-optimization - Overall performance optimization
  • v3-swarm-coordination - MCP integration with swarm coordination
  • v3-memory-unification - Memory sharing via MCP tools

Usage Examples

Complete MCP Optimization

# Full MCP server optimization
Task("MCP optimization implementation",
     "Implement all MCP performance optimizations with monitoring",
     "mcp-specialist")

Specific Optimization

# Connection pool optimization
Task("MCP connection pooling",
     "Implement advanced connection pooling with health monitoring",
     "mcp-specialist")

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

AgentDB Advanced Features

FreeSummaryAI rating 80

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

  • Sharing stored patterns across multiple AgentDB instances
  • Filtering semantic search by date or category
  • Separating knowledge and conversation databases
ruvnet/RuView98.9KUpdated Oct 20, 2026

Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.

  • Train agents from historical experience
  • Build decision-making for games and navigation
  • Combine experience from multiple agents for training
ruvnet/RuView98.9KUpdated Oct 20, 2026

AgentDB Memory Patterns

FreeSummaryAI rating 90

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

  • Add conversation history to a chat system
  • Remember user preferences across sessions
  • Reuse patterns from successful interactions
ruvnet/RuView98.9KUpdated Oct 20, 2026

AgentDB Performance Optimization

FreeSummaryAI rating 70

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

  • Reduce memory use in an AgentDB application
  • Tune search speed while preserving accuracy
  • Plan settings for millions of vectors
ruvnet/RuView98.9KUpdated Oct 20, 2026

AgentDB Vector Search

FreeSummaryAI rating 90

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

  • Build answers grounded in retrieved documents
  • Add meaning-based search to a knowledge base
  • Find documents with similar content
ruvnet/RuView98.9KUpdated Oct 20, 2026

browser

FreeSummaryAI rating 70

Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.

  • Automating login flows
  • Filling and submitting web forms
  • Extracting product names, prices, and links
ruvnet/RuView98.9KUpdated Oct 20, 2026

See all skills by ruvnet

Report a problem with this skill