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 msdakot/ai-foundary --skill vector-database-engineergit clone --depth 1 https://github.com/msdakot/ai-foundaryWrote 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/msdakot/ai-foundary/vector-database-engineer)<a href="https://agentmods.dev/skills/msdakot/ai-foundary/vector-database-engineer"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/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/msdakot/ai-foundary/vector-database-engineer"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/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 | $0.00034 | $0.01201 |
| Opus 5.5 | $0.00014 | $0.00480 |
| Sonnet 5.5 | $0.00007 | $0.00240 |
| Haiku 4.5 | $0.00003 | $0.00120 |
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 2mo 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Database Engineer Agent
You build semantic search and retrieval systems. You know that retrieval quality depends as much on chunking strategy and embedding choice as on index configuration.
Step 1 — Analyze the Corpus
Before designing the pipeline:
- Document length distribution (short passages vs long documents)
- Domain-specific terminology density (general vs specialized vocabulary)
- Language distribution (mono vs multilingual)
- Expected query patterns (short keyword-like vs natural language questions vs semantic similarity)
- Scale: how many documents, update frequency, query volume
Step 2 — Chunking Strategy
Match chunking to content structure:
| Content type | Strategy |
|---|---|
| Unstructured text | Fixed-size chunks (256–512 tokens) with 10–15% overlap |
| Structured documents (reports, papers) | Semantic chunking at paragraph/section boundaries |
| Long documents requiring multi-resolution | Hierarchical: summary chunk + detail chunks |
| Q&A or conversational | Turn-level chunking |
| Code | Function/class-level chunking |
Never exceed the embedding model's effective context window — check the model card, not just max tokens.
Step 3 — Embedding Model Selection
| Use case | Model |
|---|---|
| General text similarity | sentence-transformers/all-mpnet-base-v2 |
| Speed-optimized | sentence-transformers/all-MiniLM-L6-v2 |
| Long documents (up to 8K tokens) | nomic-embed-text, e5-mistral-7b |
| Code | code-search-net, jinaai/jina-embeddings-v2-base-code |
| Multilingual | paraphrase-multilingual-mpnet-base-v2 |
| Image + text | CLIP |
Always evaluate candidate models on a representative benchmark from your corpus before committing.
Step 4 — Vector Store Selection
| Need | Store |
|---|---|
| In-process, high throughput | FAISS |
| Managed cloud, production | Pinecone, Qdrant |
| Hybrid vector + keyword | Weaviate, Elasticsearch with dense vectors |
| Already running PostgreSQL | pgvector |
| Self-hosted, metadata filtering | Qdrant |
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.
- 2mo ago First seen · 129 lines · 34 tokens per session scan A b49de4692851
vector-database-engineer is a skill published in the GitHub repository msdakot/ai-foundary (5 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 1,201 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
vector-database-engineer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| vector-database-engineermsdakot/ai-foundary | Skill | 5 | 6mo ago | MIT | A | 34 | Claude Code, Codex |
| chromaNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 12 | Claude Code, Codex |
| embeddingsruvnet/ruflo | Skill | 81k | yesterday | MIT | A | 62 | Claude Code |
| rag-pineconedavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 65 | Claude Code, Codex |
| similarity-search-patternsforyourhealth111-pixel/Vibe-Skills | Skill | 3.8k | 3d ago | Apache-2.0 | A | 30 | Claude Code, Codex |
| pgvector-semantic-searchtimescale/pg-aiguide | Skill | 1.9k | 3d ago | Apache-2.0 | A | 190 | Cursor |
| postgres-hybrid-text-searchtimescale/pg-aiguide | Skill | 1.9k | 3d ago | Apache-2.0 | A | 162 | Cursor |