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-newton-omega-model-data-prepgit 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-newton-omega-model-data-prep)<a href="https://agentmods.dev/skills/archetypeai/agent-skills/atai-newton-omega-model-data-prep"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-newton-omega-model-data-prep/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-newton-omega-model-data-prep"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-newton-omega-model-data-prep.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 | $0.00225 | $0.02891 |
| Opus 5.5 | $0.00090 | $0.01156 |
| Sonnet 5.5 | $0.00045 | $0.00578 |
| Haiku 4.5 | $0.00022 | $0.00289 |
Grade A, and why
atai-newton-omega-model-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 25d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Newton Data Prep — Clean → Split → Featurize
A pre-modeling pipeline for time-series sensor data. Three composable building blocks that take a raw multivariate dataframe and hand back the (X, y, metadata) arrays a downstream KNN / Isolation Forest classifier expects.
Origin. The three vendored scripts originated from work by Lucas (Solutions Engineering) and have been used end-to-end in real prep pipelines. The repo copy is the source of truth — ping Lucas if you need to pull in a newer revision.
When to Apply
- User has raw multivariate sensor CSVs (1+ sensors, irregular timestamps, NaN gaps) and is about to embed them with
atai-newton-omega-model, and needs to clean the data first. - User asks "should I drop rows with NaNs?" / "how do I handle gaps?" / "what's a good train/test split for time-series?"
- User's classifier is suspiciously good or suspiciously bad and you suspect temporal leakage —
DataSplitter(mode='oot')is the fix. - User has per-sensor embeddings (from the Omega model) and needs to fold them into a single feature matrix for KNN — that's
FeaturePreparer. - User wants the "joint state" pattern described in
atai-newton-omega-modelin code form.
Use the external omega-1-4-preflight checks instead when: you want a read-only go/no-go gate before committing to a run. Preflight makes no changes to the data. This skill makes changes — block-splitting, imputation, dimensionality reduction. The two are complementary: preflight tells you whether the dataset is salvageable; this skill cleans it up.
Do not use this skill when:
- The task is video, image, or text (use
atai-newton-fusion-model). - Data is already pristine (regular sampling, no NaNs, no leakage risk) — the pipeline becomes a no-op and you can hand the dataframe directly to the downstream skill.
What ships with it
9 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/data_preprocessor.py 18 KB runs code
- references/data_splitter.py 4.3 KB runs code
- references/feature_preparer.py 7.1 KB runs code
- references/requirements.txt 256 B
- tests/__init__.py 0 B runs code
- tests/conftest.py 4.7 KB runs code
- tests/test_data_preprocessor.py 15 KB runs code
- tests/test_data_splitter.py 10.0 KB runs code
- tests/test_feature_preparer.py 12 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.
- 25d ago Changed · +1 lines 30c7301bffb2
- 1mo ago First seen · 200 lines · 225 tokens per session scan A 05b5f4ecf146
atai-newton-omega-model-data-prep is a skill published in the GitHub repository archetypeai/agent-skills (7 stars, last pushed today), licensed Apache-2.0. It adds 225 tokens to every session and 2,891 once invoked, about $0.0009 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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