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 archetypeai/agent-skills --skill atai-operational-state-monitoring-agentgit clone --depth 1 https://github.com/archetypeai/agent-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/archetypeai/agent-skills/atai-operational-state-monitoring-agent)<a href="https://agentmods.dev/skills/archetypeai/agent-skills/atai-operational-state-monitoring-agent"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-operational-state-monitoring-agent/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/archetypeai/agent-skills/atai-operational-state-monitoring-agent"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-operational-state-monitoring-agent.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 | $0.00217 | $0.04812 |
| Opus 5 | $0.00109 | $0.02406 |
| Sonnet 5 | $0.00043 | $0.00962 |
| Haiku 4.5 | $0.00022 | $0.00481 |
Grade A, and why
atai-operational-state-monitoring-agent scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST -H "Authorization: Bearer $ATAI_API_KEY" \ How it starts
The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OSM Agent — Managed State Classification via the Agents API
The OSM agent is the fully-managed counterpart to the client-side embed-and-KNN pattern in atai-newton-omega-model: instead of fanning out /query embedding calls and classifying locally, you hand the platform a CSV and the platform runs the whole graph server-side:
source → interpolate → window → windowInterpolate → samplingRate → limitValues → encoder → classifier → sink
You don't build or host anything: the platform ships canonical "OSM Quick Start" bundles with the six-state Volve classifier and its windowing already pinned. One run = one agent instance = one input file. You upload the CSV, resolve the pre-packaged bundle by name, run it, poll until terminal, and download one output CSV of per-window predictions.
When to Apply
- Classify operational states over a full CSV of sensor records with no client-side ML and no classifier of your own — the maintained bundle embeds and classifies every window
- Demo or evaluate the managed OSM path as a deployed, repeatable batch job
- Score the managed predictions on held-out slices against a ground-truth sidecar
Your own data? As of today, this skill runs the pre-packaged OSM Quick Start bundles, whose classifier is fit to the Volve six-state drilling data. To classify your own data with states that match it, contact [email protected] — Archetype AI will work with you to create agent bundles tailored to your data. (The "Bring your own classifier" section below documents the underlying mechanics.)
Do not use this skill when:
- You want interactive, per-window embeddings to do ML client-side — use
atai-newton-omega-model - The raw CSV still needs cleaning / gap-aware segmentation / normalization — see
atai-newton-omega-model-data-prep; the OSM agent assumes prepared, z-scored input - You want to run your own fitted classifier rather than the pre-packaged one — the supported path is a tailored bundle created with Archetype AI (contact [email protected]); the underlying mechanics (blueprint
osm+ afit-classifierS3 artifact) are in the "Bring your own classifier" note below
What ships with it
8 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.
- references/.env.example 570 B
- references/osm-output.csv 221 KB
- references/requirements.txt 91 B
- references/run_osm_agent.py 12 KB runs code
- references/sample_data/README.md 1.6 KB
- references/sample_data/volve_states_opt_slice_04_labels.csv 445 KB
- references/sample_data/volve_states_opt_slice_04.csv 404 KB
- tests/test_references.py 10.0 KB runs code
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.
- 12d ago First seen · 259 lines · 217 tokens per session scan A 86c6465c67b4
atai-operational-state-monitoring-agent is a skill published in the GitHub repository archetypeai/agent-skills (5 stars, last pushed today), licensed Apache-2.0. It adds 217 tokens to every session and 4,812 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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