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 pedroiff0/awesome-skills --skill rag-local-lancedbgit clone --depth 1 https://github.com/pedroiff0/awesome-skillsWrote 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/pedroiff0/awesome-skills/rag-local-lancedb)<a href="https://agentmods.dev/skills/pedroiff0/awesome-skills/rag-local-lancedb"><img src="https://agentmods.dev/badge/skills/pedroiff0/awesome-skills/rag-local-lancedb/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/pedroiff0/awesome-skills/rag-local-lancedb"><img src="https://agentmods.dev/badge/skills/pedroiff0/awesome-skills/rag-local-lancedb.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 | $0.00035 | $0.00230 |
| Opus 5.5 | $0.00014 | $0.00092 |
| Sonnet 5.5 | $0.00007 | $0.00046 |
| Haiku 4.5 | $0.00003 | $0.00023 |
Grade A, and why
rag-local-lancedb 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
Local RAG with LanceDB
This skill guides the construction and query execution of serverless, high-performance local vector databases using LanceDB and local embeddings.
When to Use
- Building offline semantic search across local Markdown files, codebases, or documentation.
- Storing vector embeddings with zero external cloud API costs.
- Performing hybrid full-text + vector similarity queries.
Quick Setup & Python Usage
import lancedb
# Connect to local database directory
db = lancedb.connect("~/.local/share/agent_rag")
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 · 32 lines · 35 tokens per session scan A 4a509153cfa0
rag-local-lancedb is a skill published in the GitHub repository pedroiff0/awesome-skills (1 stars, last pushed 4d ago), licensed MIT. It adds 35 tokens to every session and 230 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.
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