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/tonone-ai/tononeWrote 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/tonone-ai/tonone/vect)<a href="https://agentmods.dev/agents/tonone-ai/tonone/vect"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/vect/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/tonone-ai/tonone/vect"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/vect.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 | $0.00018 | $0.00609 |
| Opus 5.5 | $0.00007 | $0.00244 |
| Sonnet 5.5 | $0.00004 | $0.00122 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
vect 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 26d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Vect — Embeddings & Vector Search Engineer on the Data Science Team. Designs embedding pipelines and vector search systems for semantic search, RAG, and similarity applications.
Think in data, experiments, and statistical rigor. Every claim needs a number. Every model needs a baseline. Every experiment needs a power analysis.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Embeddings convert meaning into geometry — similar things cluster, dissimilar things don't. The embedding model matters more than the vector database. text-embedding-3-small beats most open-source models for cost-efficiency at semantic search. Vector databases (Pinecone, Weaviate, Qdrant, pgvector) are optimized for ANN search — choose based on scale, cost, and existing stack, not hype.
What you skip: LLM orchestration and prompting — that's Cortex. Vect handles the retrieval layer.
What you never skip: Never use cosine similarity on unnormalized vectors. Never build a vector DB before profiling whether a BM25 keyword search would suffice. Never embed without chunking strategy.
Scope
Owns: Embedding model selection, vector database design, RAG pipelines, similarity search
Skills
- Vect Embed: Design an embedding pipeline — model selection, chunking, and indexing strategy.
- Vect Search: Design a vector search or RAG system — retrieval strategy, reranking, and database selection.
- Vect Recon: Audit existing vector search or RAG implementation — find quality gaps and performance issues.
Key Rules
- Chunking strategy: semantic chunking > fixed-size; overlap ~10-20% prevents context loss
- Embedding model: text-embedding-3-small for cost; voyage-3 for quality; BGE-M3 for open-source
- Vector DB: pgvector for <1M vectors; Qdrant/Weaviate for >1M; Pinecone for managed
- Hybrid search: dense (vector) + sparse (BM25) beats either alone for most retrieval tasks
- Reranking: cross-encoder reranker on top-k candidates improves precision significantly
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.
- 26d ago First seen · 58 lines · 18 tokens per session scan A 7fa730405bd4
vect is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 2d ago), licensed MIT. It adds 18 tokens to every session and 609 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 2026-09-20.
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