Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tranhieutt/software_development_department --skill vector-database-engineergit clone --depth 1 https://github.com/tranhieutt/software_development_departmentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tranhieutt/software_development_department/vector-database-engineer)<a href="https://agentmods.dev/skills/tranhieutt/software_development_department/vector-database-engineer"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/vector-database-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tranhieutt/software_development_department/vector-database-engineer"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/vector-database-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.00600 |
| Opus 5 | $0.00030 | $0.00300 |
| Sonnet 5 | $0.00012 | $0.00120 |
| Haiku 4.5 | $0.00006 | $0.00060 |
Grade A, and why
vector-database-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Database Engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.
Do not use this skill when
- The task is unrelated to vector database engineer
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
Capabilities
- Vector database selection and architecture
- Embedding model selection and optimization
- Index configuration (HNSW, IVF, PQ)
- Hybrid search (vector + keyword) implementation
- Chunking strategies for documents
- Metadata filtering and pre/post-filtering
- Performance tuning and scaling
Use this skill when
- Building RAG (Retrieval Augmented Generation) systems
- Implementing semantic search over documents
- Creating recommendation engines
- Building image/audio similarity search
- Optimizing vector search latency and recall
- Scaling vector operations to millions of vectors
Workflow
- Analyze data characteristics and query patterns
- Select appropriate embedding model
- Design chunking and preprocessing pipeline
- Choose vector database and index type
- Configure metadata schema for filtering
- Implement hybrid search if needed
- Optimize for latency/recall tradeoffs
- Set up monitoring and reindexing strategies
Best Practices
- Choose embedding dimensions based on use case (384-1536)
- Implement proper chunking with overlap
- Use metadata filtering to reduce search space
- Monitor embedding drift over time
- Plan for index rebuilding
- Cache frequent queries
- Test recall vs latency tradeoffs
When to Use
- Use when Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 71 lines · 60 tokens per session scan A 02002d9f747b
vector-database-engineer is a skill published in the GitHub repository tranhieutt/software_development_department (72 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 600 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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laravel-vector-search
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vector-db-init
Interactively initializes the Vector DB plugin. Guided discovery asks which folders to index, confirms the manifest, then scaffolds vectorprofiles.json for high-performance In-Process or Native Server connections. Mandatory first step before ingestion or search.
vector-db-launch
Start the Native Python ChromaDB background server. Use when semantic search returns connection refused on port 8110, or when the user wants to enable concurrent agent read/writes.
vector-db-cleanup
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned…