Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
MemoryLayer
Semantic memory for AI agents. 95% token savings with vector search.
skill openclawclawhub Free
Semantic memory for AI agents. 95% token savings with vector search.
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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
- .gitignore, index.js, package-lock.json, package.json, README.md, 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. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- khli01
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
MemoryLayer
Semantic memory infrastructure for AI agents that actually scales.
Features
95% Token Savings - Retrieve only relevant memories Semantic Search - Find memories by meaning, not keywords Sub-200ms - Lightning-fast memory retrieval Multi-tenant - Isolated memory per agent instance
1. Sign up for FREE account
Visit https://memorylayer.clawbot.hk and sign up with Google. You'll get: 10,000 operations/month 1GB storage Community support
2. Configure credentials
# Option 1: Email/Password export MEMORYLAYER_EMAIL=your@email.com export MEMORYLAYER_PASSWORD=your_password # Option 2: API Key (recommended for production) export MEMORYLAYER_API_KEY=ml_your_api_key_here
3. Install Python SDK (if not using skill wrapper)
pip install memorylayer
Basic Example
// In your Clawdbot agent const memory = require('memorylayer'); // Store a memory await memory.remember( 'User prefers dark mode UI', { type: 'semantic', importance: 0.8 } ); // Search memories const results = await memory.search('UI preferences'); console.log(results[0].content); // "User prefers dark mode UI"
Python Example
from plugins.memorylayer import memory # Store memory.remember( "Boss prefers direct reporting with zero bullshit", memory_type="semantic", importance=0.9 ) # Search results = memory.recall("What are Boss's preferences?") for r in results: print(f"{r.relevance_score:.2f}: {r.memory.content}")
Token Savings
Before MemoryLayer: # Inject entire memory files context = open('MEMORY.md').read() # 10,500 tokens prompt = f"{context}\n\nUser: What are my preferences?" After MemoryLayer: # Inject only relevant memories context = memory.get_context("user preferences", limit=5) # ~500 tokens prompt = f"{context}\n\nUser: What are my preferences?" Result: 95% token reduction, $900/month savings at scale
memory.remember(content, options)
Store a new memory. Parameters: content (string): Memory content options.type (string): 'episodic' | 'semantic' | 'procedural' options.importance (number): 0.0 to 1.0 options.metadata (object): Additional tags/data Returns: Memory object with id
memory.search(query, limit)
Search memories semantically. Parameters: query (string): Search query (natural language) limit (number): Max results (default: 10) Returns: Array of SearchResult objects
memory.get_context(query, limit)
Get formatted context for prompt injection. Parameters: query (string): What context do you need? limit (number): Max memories (default: 5) Returns: Formatted string ready for prompt
memory.stats()
Get usage statistics. Returns: Object with total_memories, memory_types, operations_this_month
Memory Types
Episodic - Events and experiences memory.remember('Deployed MemoryLayer on 2026-02-03', { type: 'episodic' }); Semantic - Facts and knowledge memory.remember('Boss prefers concise reports', { type: 'semantic' }); Procedural - How-to and processes memory.remember('To restart server: ssh root@... && systemctl restart...', { type: 'procedural' });
Metadata Tagging
memory.remember('User likes blue', { type: 'semantic', metadata: { category: 'preferences', subcategory: 'colors', source: 'user_profile' } });
Usage Tracking
const stats = await memory.stats(); console.log(`Total memories: ${stats.total_memories}`); console.log(`Operations this month: ${stats.operations_this_month}`); console.log(`Plan: ${stats.plan} (${stats.operations_limit}/month)`);
Pricing
FREE Plan (Current) 10,000 operations/month 1GB storage Community support Pro Plan ($99/mo) 1M operations/month 10GB storage Email support 99.9% SLA Enterprise (Custom) Unlimited operations Unlimited storage Dedicated support Self-hosted option Custom SLA
Support
Documentation: https://memorylayer.clawbot.hk/docs API Reference: https://memorylayer.clawbot.hk/api Community: Discord (link in docs) Issues: GitHub (link in docs)
Links
Homepage: https://memorylayer.clawbot.hk Dashboard: https://dashboard.memorylayer.clawbot.hk API Docs: https://memorylayer.clawbot.hk/docs Python SDK: https://pypi.org/project/memorylayer (when published)
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Config1 Scripts1 Files
- SKILL.md
- README.md
- index.js
- package-lock.json
- package.json
- .gitignore