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 nebius/nebius-physical-ai --skill lancedbgit clone --depth 1 https://github.com/nebius/nebius-physical-aiWrote 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/nebius/nebius-physical-ai/lancedb)<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/lancedb"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/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/nebius/nebius-physical-ai/lancedb"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/lancedb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.00833 |
| Opus 5 | $0.00023 | $0.00417 |
| Sonnet 5 | $0.00009 | $0.00167 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
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 9d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LanceDB
When To Use
Use this skill for vector-search workbench changes, perception dataset imports, BDD100K failure-mode slices, materialized views, CLIP embedding backfills, and LanceDB CLI/API/SDK parity reviews.
Procedure
-
Pick the data shape first. LanceDB is best for frame-aligned records such as image paths, annotations, metadata, and vectors. It is not the right store for raw multi-rate sensor streams.
-
Create or inspect tables before ingestion:
npa workbench lancedb create-table --help npa workbench lancedb query --help -
Import supported datasets through current commands:
npa workbench lancedb import-lerobot --help npa workbench lancedb import-bdd100k --help -
Add derived fields through
backfill, then materialize reusable SQL slices withcreate-mv,refresh-mv, andquery-table.
Three-Tier Contract
- CLI:
deploy,status,list,create-table,query,import-lerobot,import-bdd100k,backfill,create-mv,refresh-mv, andquery-table. - SDK/API: keep table import, backfill, and query behavior in shared implementation paths so CLI, SDK, and service endpoints produce equivalent manifests and row counts.
- YAML: workflow tasks should pass S3-backed LanceDB URIs and table names through environment variables, not hardcoded project paths.
BDD100K Contract
BDD100K UDFs:
has_personhas_riderperson_bbox_area_pctdhashis_duplicateclip_embedding
PERSON_CATEGORIES = {"person", "pedestrian"}. Real BDD100K uses
pedestrian; synthetic data may use person. Both must be accepted.
Materialized views are SQL-defined failure-mode slices such as rider_train,
nighttime_person_train, and distant_person_train. CLIP embeddings are
512-dimensional float32, use a GPU UDF, and route to H100.
The current GPU-capable image is
npa-lancedb:cuda13-b300-0.30.3-sm80-sm90-sm100-sm103-sm120-20260803T031514Z
(sha256:a303b53d0769e612101d468ba957656997838f4b8e6a03430f2b5a2c89e3f8b5
in both registries). It was measured on physical B200, B300, H100, and RTX PRO
6000 by embedding three images with CLIP, checking normalized distinct vectors,
writing them to Lance, and checking top-1 self-search. The historical 0.30.3
image predates the current transformers return-type fix and must not be used for
the CLIP path.
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.
- 9d ago First seen · 90 lines · 46 tokens per session scan A 5ddee0232ce8
lancedb is a skill published in the GitHub repository nebius/nebius-physical-ai (27 stars, last pushed today), licensed Apache-2.0. It adds 46 tokens to every session and 833 once invoked, about $0.0002 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 2026-08-30.
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