Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Smart Cache
Intelligent caching with LRU/LFU strategies and TTL management
skill openclawclawhub Free
Intelligent caching with LRU/LFU strategies and TTL management
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- raghulpasupathi
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Smart Cache
High-performance caching with multiple eviction strategies.
Implementation
class SmartCache { constructor(options = {}) { this.strategy = options.strategy || 'lru'; // 'lru' or 'lfu' this.maxSize = options.maxSize || 500; this.defaultTTL = options.defaultTTL || 3600000; // 1 hour this.cache = new Map(); this.accessCount = new Map(); this.accessOrder = []; } set(key, value, ttl = this.defaultTTL) { // Evict if at capacity if (this.cache.size >= this.maxSize && !this.cache.has(key)) { this.evict(); } const entry = { value, expiresAt: Date.now() + ttl, createdAt: Date.now() }; this.cache.set(key, entry); this.accessCount.set(key, 0); this.updateAccessOrder(key); return true; } get(key) { const entry = this.cache.get(key); if (!entry) { return { hit: false, value: null }; } // Check TTL if (Date.now() > entry.expiresAt) { this.delete(key); return { hit: false, value: null, reason: 'expired' }; } // Update access tracking this.accessCount.set(key, (this.accessCount.get(key) || 0) + 1); this.updateAccessOrder(key); return { hit: true, value: entry.value }; } delete(key) { this.cache.delete(key); this.accessCount.delete(key); this.accessOrder = this.accessOrder.filter(k => k !== key); } evict() { if (this.strategy === 'lru') { this.evictLRU(); } else if (this.strategy === 'lfu') { this.evictLFU(); } } evictLRU() { // Remove least recently used if (this.accessOrder.length > 0) { const keyToEvict = this.accessOrder[0]; this.delete(keyToEvict); } } evictLFU() { // Remove least frequently used let minCount = Infinity; let keyToEvict = null; for (const [key, count] of this.accessCount.entries()) { if (count < minCount) { minCount = count; keyToEvict = key; } } if (keyToEvict) { this.delete(keyToEvict); } } updateAccessOrder(key) { // Remove from current position this.accessOrder = this.accessOrder.filter(k => k !== key); // Add to end (most recent) this.accessOrder.push(key); } clear() { this.cache.clear(); this.accessCount.clear(); this.accessOrder = []; } getStats() { return { size: this.cache.size, maxSize: this.maxSize, strategy: this.strategy, hitRate: this.calculateHitRate() }; } calculateHitRate() { // Simplified - would track actual hits/misses in production return Math.round((this.cache.size / this.maxSize) * 100); } } // Export for OpenClaw module.exports = { SmartCache };
Usage
const cache = new skills.smartCache.SmartCache({ strategy: 'lru', maxSize: 500, defaultTTL: 3600000 }); // Set value cache.set('key1', { data: 'value' }, 60000); // Get value const result = cache.get('key1'); if (result.hit) { console.log('Cache hit:', result.value); } // Stats console.log(cache.getStats());
Configuration
{ "strategy": "lru", "maxSize": 500, "defaultTTL": 3600000 }
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md