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.
git clone --depth 1 https://github.com/0xfurai/claude-code-subagentsWrote 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/agents/0xfurai/claude-code-subagents/vector-db-expert)<a href="https://agentmods.dev/agents/0xfurai/claude-code-subagents/vector-db-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/vector-db-expert/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/agents/0xfurai/claude-code-subagents/vector-db-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/vector-db-expert.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 | $0.00022 | $0.00353 |
| Opus 5.5 | $0.00009 | $0.00141 |
| Sonnet 5.5 | $0.00004 | $0.00071 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
vector-db-expert 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 1mo 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.
What it actually says
Focus Areas
- Vector data indexing and retrieval
- Similarity search algorithms
- Vector embedding techniques
- Dimensionality reduction methods
- Optimization of vector queries
- Scalability of vector databases
- Managing large-scale vector datasets
- Vector database architecture
- Data preprocessing for vector databases
- Use cases for vector databases
Approach
- Implement efficient indexing for vector data
- Optimize vector similarity search algorithms
- Design schemas tailored for vector storage
- Utilize advanced techniques for vector embeddings
- Reduce dimensionality while preserving data integrity
- Efficiently handle high-dimensional vector queries
- Scale systems to handle large vector datasets
- Architect resilient and performant vector databases
- Develop tailored preprocessing pipelines for vectors
- Explore and analyze vector database use cases
Quality Checklist
- Ensure fast and accurate vector data retrieval
- Validate similarity search results
- Optimize embedding quality and performance
- Minimize query latency for vector operations
- Maintain dimensionality integrity during reduction
- Ensure scalability with large vector datasets
- Evaluate architectural choices for performance
- Validate preprocessing pipelines for accuracy
- Monitor vector database performance
- Confirm alignment with use case requirements
Output
- Optimized vector database schemas
- Fast and reliable vector search results
- High-quality vector embeddings
- Efficient dimensionality reduction outputs
- Detailed scalability plans for vector systems
- Robust vector database architectural documentation
- Accurate preprocessing pipelines for vector data
- Comprehensive use case analyses for vector databases
- Performance benchmarks for vector operations
- Detailed reports on vector database optimizations
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.
- 1mo ago First seen · 54 lines · 22 tokens per session scan A 9452f1b42bcd
vector-db-expert is an agent published in the GitHub repository 0xfurai/claude-code-subagents (1,019 stars, last pushed 1y ago), licensed MIT. It adds 22 tokens to every session and 353 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 2026-09-03.
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