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/pedrol-cmd/brain-drinWrote 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/commands/pedrol-cmd/brain-drin/vector-db)<a href="https://agentmods.dev/commands/pedrol-cmd/brain-drin/vector-db"><img src="https://agentmods.dev/badge/commands/pedrol-cmd/brain-drin/vector-db.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.00090 |
| Opus 5 | $0.00009 | $0.00045 |
| Sonnet 5 | $0.00003 | $0.00018 |
| Haiku 4.5 | $0.00002 | $0.00009 |
Grade A, and why
vector-db 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 3d 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
/vector-db
Mantém o cérebro do seu sistema RAG atualizado e eficiente.
Usage
- Ingestão de novos documentos.
- Otimização de chunks e embeddings.
- Pruning de dados obsoletos.
Specialist Agent
@data-engineer
skill: drin-vector-database-sync
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.
- 3d ago First seen · 18 lines · 17 tokens per session scan A 5a5ecc792f1c
vector-db is a command published in the GitHub repository pedrol-cmd/brain-drin (11 stars, last pushed 4mo ago), licensed MIT. It adds 17 tokens to every session and 90 once invoked, about $0.0001 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.
Other commands, from other repositories
querying
Query documents from a search index using type-safe filters with support for pagination, sorting, field selection, scoring, and highlighting. Count matching documents efficiently without returning results.
agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
ingest
Manually add knowledge to the Weaviate store.
inference.embed
Embed one or more texts into vectors.
vector.batch_exists
Check existence for multiple vector keys.
vector.batch_upsert
Upsert multiple vectors in one itemwise batch.