Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· Developer Tools
QMD Memory
Enables local hybrid memory search and embedding using QMD to reduce API costs by $50-300/month with automatic setup, smart indexing, and multi-agent sharing.
skill openclawclawhub Free
Enables local hybrid memory search and embedding using QMD to reduce API costs by $50-300/month with automatic setup, smart indexing, and multi-agent sharing.
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
- README.md, SKILL.md, scripts/calculate-savings.sh, scripts/refresh.sh, scripts/serve.sh, scripts/setup.sh
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
- asabovetech
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Local Hybrid Search β Save $50-300/month in API Costs
Author: As Above Technologies Version: 1.0.0 ClawHub: [Coming Soon]
API Costs You're Paying Now
OperationAPI CostFrequencyMonthly Costmemory_search (embedding)$0.02-0.0550-200/day$30-300Context retrieval$0.01-0.03100+/day$30-90Semantic queries$0.03-0.0820-50/day$18-120TOTAL$78-510/month
With QMD Local
OperationCostWhyAll searches$0Runs on your machineEmbeddings$0Local GGUF modelsRe-ranking$0Local LLM Your savings: $50-300+/month One-time setup. Forever free searches.
π QUICK START
# Install the skill clawhub install asabove/qmd-memory # Run setup (installs QMD, configures collections) openclaw skill run qmd-memory setup # That's it. Your memory is now supercharged.
1. Automatic Collection Setup
Based on your workspace structure, we create optimized collections: β workspace β Core agent files (MEMORY.md, SOUL.md, etc.) β daily-logs β memory/*.md daily logs β intelligence β intelligence/*.md (if exists) β projects β projects/**/*.md (if exists) β documents β Any additional doc folders you specify
2. Smart Context Descriptions
We add context to each collection so QMD understands what's where: qmd://workspace β "Agent identity and configuration files" qmd://daily-logs β "Daily work logs and session history" qmd://intelligence β "Analysis, research, and reference documents"
3. Pre-configured Cron Jobs
# Auto-update index (nightly at 3am) 0 3 * * * qmd update && qmd embed # Keep your memory fresh without thinking about it
4. OpenClaw Integration
Memory search now uses QMD automatically: memory_search β routes to QMD hybrid search memory_get β retrieves from QMD collections Results include collection context
5. Multi-Agent MCP Server (Optional)
# Start shared memory server openclaw skill run qmd-memory serve # All your agents can now query collective memory # Forge, Thoth, Axis β shared knowledge base
π SEARCH MODES
ModeCommandBest ForKeywordqmd search "query"Exact matches, fastSemanticqmd vsearch "query"Conceptual similarityHybridqmd query "query"Best quality (recommended)
Example Queries
# Find exact mentions qmd search "Charlene" -n 5 # Find conceptually related content qmd vsearch "how should we handle customer complaints" # Best quality β expansion + reranking qmd query "what decisions did we make about pricing strategy" # Search specific collection qmd search "API keys" -c workspace
Add Custom Collections
openclaw skill run qmd-memory add-collection ~/Documents/research --name research
Add Context
openclaw skill run qmd-memory add-context qmd://research "Market research and competitive analysis"
Refresh Index
openclaw skill run qmd-memory refresh
Trading/Investing Workspace
openclaw skill run qmd-memory template trading Creates: intelligence β Trading systems, dashboards, signals market-data β Price history, analysis research β Due diligence, reports daily-logs β Trade journal
Content Creator Workspace
openclaw skill run qmd-memory template content Creates: articles β Published content drafts β Work in progress research β Source material ideas β Brainstorms, notes
Developer Workspace
openclaw skill run qmd-memory template developer Creates: docs β Documentation notes β Technical notes decisions β ADRs, architecture decisions snippets β Code snippets, examples
π COST SAVINGS CALCULATOR
Run this to see your estimated savings: openclaw skill run qmd-memory calculate-savings Output: Your Current API Memory Costs (estimated): memory_search calls/day: ~75 Average cost per call: $0.03 Monthly API cost: $67.50 With QMD Local: Monthly cost: $0.00 YOUR MONTHLY SAVINGS: $67.50 YOUR ANNUAL SAVINGS: $810.00 ROI on skill purchase: 40x (if skill was $20)
Models Used (Auto-Downloaded)
ModelPurposeSizeembeddinggemma-300M-Q8_0Vector embeddings~300MBqwen3-reranker-0.6b-q8_0Re-ranking results~640MBqmd-query-expansion-1.7B-q4_k_mQuery expansion~1.1GB Total: ~2GB (one-time download)
System Requirements
Node.js >= 22 ~3GB disk space (models + index) ~2GB RAM during embedding (then minimal)
Where Data is Stored
~/.cache/qmd/ βββ index.sqlite # Search index βββ models/ # GGUF models βββ mcp.pid # MCP server PID (if running)
π€ SUPPORT
Questions? GitHub Issues: github.com/asabove/qmd-memory-skill Discord: As Above community Email: support@asabove.tech Found it valuable? Star us on ClawHub Share with other OpenClaw users Subscribe to our newsletter for more agent optimization tips
π LICENSE
MIT β Use freely, modify as needed. QMD itself is created by Tobi LΓΌtke (github.com/tobi/qmd). This skill provides easy OpenClaw integration. "Stop paying for memory. Start compounding knowledge." As Above Technologies β Agent Infrastructure for Humans
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
4 Scripts2 Docs
- SKILL.md
- README.md
- scripts/calculate-savings.sh
- scripts/refresh.sh
- scripts/serve.sh
- scripts/setup.sh