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-fusion-modelgit 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-fusion-model)<a href="https://agentmods.dev/skills/archetypeai/agent-skills/atai-newton-fusion-model"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-newton-fusion-model/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-fusion-model"><img src="https://agentmods.dev/badge/skills/archetypeai/agent-skills/atai-newton-fusion-model.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 | $0.00223 | $0.05105 |
| Opus 5 | $0.00112 | $0.02552 |
| Sonnet 5 | $0.00045 | $0.01021 |
| Haiku 4.5 | $0.00022 | $0.00511 |
Grade A, and why
atai-newton-fusion-model 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 $ATAI_API_ENDPOINT/v0.5/files \ How it starts
The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Newton C 2.6 Fusion Model — Multimodal /query in One Hop
Single stateless POST to /query for text, image, or video reasoning against the C 2.6 fusion checkpoint. No Lens session, no batch pipeline, no SSE. One call → one response.
When to Apply
- User wants to describe / classify / extract structure from an image with one API call
- User wants the same for a short video in a single
/querycall - User wants stateless multimodal reasoning (no per-session warmup, no orphan cleanup)
- User wants JSON output from a multimodal input (the prompt is the schema)
- User is building a serverless / per-request handler where a streaming Lens session would be the wrong shape
Do not use this skill when:
- The workload is live, streaming video → text from a long-running camera feed — a session-based activity monitor is the right shape, not stateless
/query - The workload is a large multi-file batch job
- The task is time-series classification via embeddings (KNN / anomaly detection)
The Model Identifier
Newton::c2_6_8b_fp8_260424d7a55d5e
The full Newton:: prefix and the _fp8_ segment are required. Variants that omit either are rejected with 400 invalid_model_version:
c2_6_8b_fp8_260424d7a55d5e ❌ missing Newton:: prefix
Newton::c2_6_8b_260424d7a55d5e ❌ missing _fp8_
c2_6_8b_260424d7a55d5e ❌ both missing
This was probed end-to-end against https://api.u1.archetypeai.app/v0.5/query on prod.
Endpoint
POST {ATAI_API_ENDPOINT}/v0.5/query
Authorization: Bearer <API_KEY>
Content-Type: application/json
Both ATAI_API_KEY and ATAI_API_ENDPOINT are required — there is no default endpoint, so a wrong-deployment mistake fails loudly at startup instead of silently at query time. Prod is https://api.u1.archetypeai.app/v0.5. The same endpoint serves all three modalities below — what changes is the request body.
Wire Shapes
Text + JSON
{
"query": "Classify the operational state. Respond with ONLY the JSON object.",
"instruction_prompt": "You output {\"state\": \"...\", \"confidence\": <0..1>}.",
"file_ids": [],
"model": "Newton::c2_6_8b_fp8_260424d7a55d5e",
"max_new_tokens": 300
}
What ships with it
13 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/_common.py 5.5 KB runs code
- references/.env.example 216 B
- references/csv_vs_txt_proof.py 4.9 KB runs code
- references/image_query.py 5.6 KB runs code
- references/requirements.txt 112 B
- references/sample_assets/1_pass_2_pass_3_pass_B.mp4 7622 KB
- references/sample_assets/assembly_after.png 376 KB
- references/sample_assets/assembly_before.png 394 KB
- references/sample_assets/README.md 1.1 KB
- references/sample_assets/wind-turbines.png 1911 KB
- references/text_query.py 7.7 KB runs code
- references/video_query.py 5.6 KB runs code
- tests/test_references.py 11 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 · 296 lines · 223 tokens per session scan A 0d95081e6a05
atai-newton-fusion-model is a skill published in the GitHub repository archetypeai/agent-skills (5 stars, last pushed today), licensed Apache-2.0. It adds 223 tokens to every session and 5,105 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.
Other skills, from other repositories
tao-finetune-nv-tesseract-ad-diffusion
NV-Tesseract AD Diffusion — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune NV-Tesseract", "run AD diffusion inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune ad-diffusion", "use…
tao-finetune-nv-tesseract-forecasting
NV-Tesseract Forecasting — transformer-based multivariate time series forecasting with DARR (context-enhanced kNN retrieval), interpretability, and fine-tuning. Use when the user asks to "forecast with NV-Tesseract", "run forecasting inference", "use performforecasting", "DARR mode", "context-enhanced forecasting"…
nixtla-model-benchmarker
Generate benchmarking pipelines to compare forecasting models and summarize accuracy/speed trade-offs. Use when evaluating TimeGPT vs StatsForecast/MLForecast/NeuralForecast on a dataset. Trigger with "benchmark models", "compare TimeGPT vs StatsForecast", or "model selection".
nixtla-research-assistant
Research and summarize Nixtla ecosystem updates and time-series forecasting content from the web and GitHub. Use when gathering release notes, recent changes, or best-practice references. Trigger with "Nixtla updates", "what's new with TimeGPT", or "find time-series papers".
timegpt-pipeline-builder
Generate production-ready TimeGPT forecasting pipeline code from requirements. Use when scaffolding a pipeline with validation, logging, visualization, and repeatable runs. Trigger with "create TimeGPT pipeline", "build TimeGPT integration", or "generate forecast code".
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…