What is atai-operational-state-monitoring-agent-data-prep?

A local preparation workflow for labelled sensor recordings used by Archetype AI's Operational State Monitoring agent. It puts recordings on a consistent sample grid, preserves gaps, checks state labels, and builds the file layout required by the platform.

Use it to split recordings at gaps, check that states are distinguishable, divide data for training and testing, and create validated role files for upload.

Incorrect gaps, labels, or train/test files can make an optimization fail or produce a zero score without a clear error.

atai-operational-state-monitoring-agent-data-pr

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 archetypeai/agent-skills --skill atai-operational-state-monitoring-agent-data-prep

Clone the repo

git clone --depth 1 https://github.com/archetypeai/agent-skills

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/archetypeai/agent-skills/atai-operational-state-monitoring-agent-data-prep/github.svg)](https://agentmods.dev/skills/archetypeai/agent-skills/atai-operational-state-monitoring-agent-data-prep)

Your own site

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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/archetypeai/agent-skills/atai-operational-state-monitoring-agent-data-prep"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-operational-state-monitoring-agent-data-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 239 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,206 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.

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	$0.00239	$0.04206
Opus 5.5	$0.00096	$0.01682
Sonnet 5.5	$0.00048	$0.00841
Haiku 4.5	$0.00024	$0.00421

Measured 9d ago against content hash 9950c37b17e1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

atai-operational-state-monitoring-agent-data-prep 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.

The scan reads SKILL.md. This mod also ships 7 executable files (references/build_roles.py, references/check_states.py, references/osm_common.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/atai-operational-state-monitoring-agent-data-prep/SKILL.md · 308 lines

How it starts

The opening of the file, as written. The whole thing — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OSM Data Prep — Labelled Recordings → Role Files

The OSM agent learns operational states (drilling / tripping, fill / wash / spin / drain, …) from labelled recordings and classifies every window of new ones. Before anything reaches the platform, the recordings have to become role files: a small, strict layout the Optimizations and Evals APIs accept. Most ways to get it wrong don't error: an optimization with too many training files never starts, one scored window across a time gap fails the whole trial, and a state missing from the test set scores 0. This skill builds the layout and checks every rule before you upload.

recordings (any rate, gaps, jitter)          role files (the contract)
  + a per-row state label        prepare.py   ─┐
  + recordings.csv (index)   →   check_states │→  build_roles.py  →  preflight_roles.py  →  OSM agent skill, Path 2
                                 split_roles  ─┘

Everything here is local and needs no API key. The contract itself is in references/role-files.md; the platform rules in it were measured on the dev deployment (2026-09-30) while building the LARCO washing-machine examples (the quickstart, and the full example).

When to Apply

You have labelled recordings (one state per row, or per stretch) from one or more assets, and want an OSM agent that recognises those states
An OSM optimization stays pending forever, or fails with eval-mode test data must not contain windows a validation node rejected — preflight your role files
You want to know whether your states are separable from the sensor before spending platform time on them

Do not use this skill when:

You only want to run the maintained Volve bundle on one CSV — that's atai-operational-state-monitoring-agent, Path 1
You're embedding windows yourself over /query for a local KNN — use atai-newton-omega-model-data-prep (its FeaturePreparer has no role here: the platform embeds)
Your raw data first needs heavy cleaning (imputing short dropouts, diagnosing sensors): do that with atai-newton-omega-model-data-prep's DataPreprocessor, then come back here

Read the full file on GitHub · 308 lines

Files

What ships with it

20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

9d ago Changed 9950c37b17e1
15d ago First seen · 308 lines · 239 tokens per session scan A 2f0fb23e434a

Subscribe to this mod's changes

atai-operational-state-monitoring-agent-data-prep is a skill published in the GitHub repository archetypeai/agent-skills (6 stars, last pushed today), licensed Apache-2.0. It adds 239 tokens to every session and 4,206 once invoked, about $0.0010 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 2026-10-02.

Analytics and metrics Claude Code Hardware and IoT agent-skills newton physical-ai sensor-data time-series

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Guard mode for security monitoring. Two uses — (1) Toggle on/off when a friend says "guard mode", "watch the house", "I'm going out", or similar. (2) Handle any sensing event that carries a [guard-active] tag (e.g. [sensing:presence.enter][guard-active], [sensing:motion][guard-active]) with dramatic shock/curious…

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