Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Embeddings
Generate, store, and search vector embeddings with provider selection, chunking strategies, and similarity search optimization.
skill openclawclawhub Free
Generate, store, and search vector embeddings with provider selection, chunking strategies, and similarity search optimization.
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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
- SKILL.md, chunking.md, providers.md, search.md, storage.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
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. 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
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
When to Use
User wants to convert text/images to vectors, build semantic search, or integrate embeddings into applications.
Quick Reference
TopicFileProvider comparison & selectionproviders.mdChunking strategies & codechunking.mdVector database patternsstorage.mdSearch & retrieval tuningsearch.md
Core Capabilities
Generate embeddings โ Call provider APIs (OpenAI, Cohere, Voyage, local models) Chunk content โ Split documents with overlap, semantic boundaries, token limits Store vectors โ Insert into Pinecone, Weaviate, Qdrant, pgvector, Chroma Similarity search โ Query with top-k, filters, hybrid search Batch processing โ Handle large datasets with rate limiting and retries Model comparison โ Evaluate embedding quality for specific use cases
Decision Checklist
Before recommending approach, ask: What content type? (text, code, images, multimodal) Volume and update frequency? Latency requirements? (real-time vs batch) Budget constraints? (API costs vs self-hosted) Existing infrastructure? (cloud provider, database)
Critical Rules
Same model everywhere โ Query embeddings MUST use identical model as document embeddings Normalize before storage โ Most similarity metrics assume unit vectors Chunk with overlap โ 10-20% overlap prevents context loss at boundaries Batch API calls โ Never embed one item at a time in production Cache embeddings โ Regenerating is expensive; store with source hash Monitor dimensions โ Higher isn't always better; 768-1536 is usually optimal
Provider Quick Selection
NeedProviderWhyBest quality, any costOpenAI text-embedding-3-largeTop benchmarksCost-sensitiveOpenAI text-embedding-3-small5x cheaper, 80% qualityMultilingualCohere embed-multilingual-v3100+ languagesCode/technicalVoyage voyage-code-2Optimized for codePrivacy/offlineLocal (e5, bge, nomic)No data leaves machineImagesOpenAI CLIP, Cohere multimodalCross-modal search
Common Patterns
# Batch embedding with retry def embed_batch(texts, model="text-embedding-3-small"): results = [] for chunk in batched(texts, 100): # API limit response = client.embeddings.create(input=chunk, model=model) results.extend([e.embedding for e in response.data]) return results # Similarity search with filter results = index.query( vector=query_embedding, top_k=10, filter={"category": "technical"}, include_metadata=True )
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
5 Docs
- SKILL.md
- chunking.md
- providers.md
- search.md
- storage.md