A system for turning raw production sensor data into a checked, organised, versioned dataset of record. A dataset of record is the trusted source that teams use for later searches and analysis.

Use it to ingest sensor data, validate manifests, curate records by event and location, query the dataset, check dataset versions, and inspect system information through the command line or software interfaces.

It brings data ingestion, validation, curation, and querying into one workflow instead of leaving them as disconnected tools. Versioning helps track which data was used and how it changed.

dataset — Skill for Claude Code and Codex by nebius

Install

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.

Any agent

npx skills add nebius/nebius-physical-ai --skill dataset

Clone the repo

git clone --depth 1 https://github.com/nebius/nebius-physical-ai

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/dataset/github.svg)](https://agentmods.dev/skills/nebius/nebius-physical-ai/dataset)

Your own site

<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/dataset"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/dataset/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nebius/nebius-physical-ai/dataset"><img src="https://agentmods.dev/badge/skills/nebius/nebius-physical-ai/dataset.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,079 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.01079
Opus 5	$0.00017	$0.00540
Sonnet 5	$0.00007	$0.00216
Haiku 4.5	$0.00003	$0.00108

Measured yesterday against content hash a01ebdcfb097, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

dataset 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 yesterday.

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.

skills/tools/dataset/SKILL.md · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dataset (Dataset-of-Record)

A unified ingestion / validation / curation layer that turns raw production sensor data into a queryable, versioned dataset-of-record, filterable by event, location, and quality. It composes existing primitives (FiftyOne for curation/visualization, LanceDB for the vector/metadata query index, S3 as the bus) behind one tool instead of leaving them disconnected.

Three-access pattern

Source of truth is the FastAPI service (npa/src/npa/workbench/dataset/service.py). The CLI (npa/src/npa/cli/workbench/dataset.py) and SDK (npa/src/npa/sdk/workbench/dataset.py) are thin clients. Do not duplicate logic across layers.

Interfaces

CLI:

npa workbench dataset ingest --input-path <s3> --output-path <s3> --dataset-id <id>
npa workbench dataset validate --input-path <s3-manifest> --output-path <s3>
npa workbench dataset curate --input-path <s3-manifest> --output-path <s3> --event <e> --location <l>
npa workbench dataset query --input-path <s3-manifest> --event <e> --location <l>
npa workbench dataset status --dataset-id <id> --version <v>
npa workbench dataset system-info
npa workbench dataset list

Endpoints: /health, /status, /system-info, /list, POST /ingest, POST /validate, POST /curate, GET /query.

API contract

POST /ingest: pull raw sensor data from --input-path, validate against the declared sensor schema, normalize to canonical records, and register a versioned manifest at --output-path (schema npa.dataset.manifest.v1: dataset id + version, record count, sensor modalities, source lineage, per-record S3 pointers, quality stats).
POST /validate: schema + quality-metric validation (completeness, corruption, per-sensor sanity); emits npa.dataset.validation_report.v1.
POST /curate: filter/slice by event of interest, location, and quality metric; writes a derived version whose manifest records lineage back to the parent (parent dataset id/version + filter predicate).
GET /query: query records by event/location/quality facets. Backed by the LanceDB index when --lancedb-endpoint is set; falls back to the manifest so the tool works without a running LanceDB.

Read the full file on GitHub · 96 lines

Changes

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.

yesterday Changed a01ebdcfb097
9d ago First seen · 96 lines · 35 tokens per session scan A 6bda3db2d86d

Subscribe to this mod's changes

dataset is a skill published in the GitHub repository nebius/nebius-physical-ai (27 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 1,079 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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