synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill timesfm-forecastinggit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/timesfm-forecasting)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/timesfm-forecasting"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/timesfm-forecasting/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/synthetic-sciences/openscience/timesfm-forecasting"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/timesfm-forecasting.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 | $0.00073 | $0.04272 |
| Opus 5.5 | $0.00029 | $0.01709 |
| Sonnet 5.5 | $0.00015 | $0.00854 |
| Haiku 4.5 | $0.00007 | $0.00427 |
Grade A, and why
timesfm-forecasting 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 1mo 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.
This is a copy
94% identical to timesfm-forecasting — 488 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 416 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TimesFM Forecasting
Overview
TimesFM (Time Series Foundation Model) is a pretrained decoder-only foundation model developed by Google Research for time-series forecasting. It works zero-shot — feed it any univariate time series and it returns point forecasts with calibrated quantile prediction intervals, no training required.
This skill wraps TimesFM for safe, agent-friendly local inference. It includes a mandatory preflight system checker that verifies RAM, GPU memory, and disk space before the model is ever loaded so the agent never crashes a user's machine.
Key numbers: TimesFM 2.5 uses 200M parameters (~800 MB on disk, ~1.5 GB in RAM on CPU, ~1 GB VRAM on GPU). The archived v1/v2 500M-parameter model needs ~32 GB RAM. Always run the system checker first.
When to Use This Skill
Use this skill when:
- Forecasting any univariate time series (sales, demand, sensor, vitals, price, weather)
- You need zero-shot forecasting without training a custom model
- You want probabilistic forecasts with calibrated prediction intervals (quantiles)
- You have time series of any length (the model handles 1–16,384 context points)
- You need to batch-forecast hundreds or thousands of series efficiently
- You want a foundation model approach instead of hand-tuning ARIMA/ETS parameters
Do not use this skill when:
- You need classical statistical models with coefficient interpretation → use
statsmodels - You need time series classification or clustering → use
aeon - You need multivariate vector autoregression or Granger causality → use
statsmodels - Your data is tabular (not temporal) → use
scikit-learn
Note on Anomaly Detection: TimesFM does not have built-in anomaly detection, but you can use the quantile forecasts as prediction intervals — values outside the 90% CI (q10–q90) are statistically unusual. See the
examples/anomaly-detection/directory for a full example.
⚠️ Mandatory Preflight: System Requirements Check
What ships with it
30 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.
- examples/anomaly-detection/detect_anomalies.py 17 KB runs code
- examples/anomaly-detection/output/anomaly_detection.json 8.8 KB
- examples/anomaly-detection/output/anomaly_detection.png 212 KB
- examples/covariates-forecasting/demo_covariates.py 19 KB runs code
- examples/covariates-forecasting/output/covariates_data.png 448 KB
- examples/covariates-forecasting/output/covariates_metadata.json 1.5 KB
- examples/covariates-forecasting/output/sales_with_covariates.csv 7.2 KB
- examples/global-temperature/generate_animation_data.py 4.9 KB runs code
- examples/global-temperature/generate_gif.py 6.5 KB runs code
- examples/global-temperature/generate_html.py 21 KB runs code
- examples/global-temperature/output/animation_data.json 130 KB
- examples/global-temperature/output/forecast_animation.gif 776 KB
- examples/global-temperature/output/forecast_output.csv 1.5 KB
- examples/global-temperature/output/forecast_output.json 4.4 KB
- examples/global-temperature/output/forecast_visualization.png 153 KB
- examples/global-temperature/output/interactive_forecast.html 149 KB
- examples/global-temperature/README.md 5.6 KB
- examples/global-temperature/run_example.sh 1.5 KB runs code
- examples/global-temperature/run_forecast.py 5.4 KB runs code
- examples/global-temperature/temperature_anomaly.csv 591 B
- examples/global-temperature/visualize_forecast.py 3.2 KB runs code
- references/api_reference.md 7.8 KB
- references/data_preparation.md 7.0 KB
- references/examples_and_validation.md 6.2 KB
- references/output_and_config.md 3.7 KB
- references/performance_tuning.md 2.2 KB
- references/system_requirements.md 5.7 KB
- references/workflows.md 3.7 KB
- scripts/check_system.py 16 KB runs code
- scripts/forecast_csv.py 8.5 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.
- 1mo ago Changed · +5 lines fa4083dd5f02
- 1mo ago First seen · 411 lines · 73 tokens per session scan A 10195de7cc4a
timesfm-forecasting is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 73 tokens to every session and 4,272 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to timesfm-forecasting, differing in 488 lines, and is treated as a copy.
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