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 Lzy599775/agent-auto-sci-skills --skill timesfm-forecastinggit clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-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/lzy599775/agent-auto-sci-skills/timesfm-forecasting)<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/timesfm-forecasting"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/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/lzy599775/agent-auto-sci-skills/timesfm-forecasting"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/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 | $0.04201 |
| Opus 5 | $0.00036 | $0.02100 |
| Sonnet 5 | $0.00015 | $0.00840 |
| Haiku 4.5 | $0.00007 | $0.00420 |
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 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.
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
100% identical to timesfm-forecasting — 0 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 — 409 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.
- 12d ago First seen · 409 lines · 73 tokens per session scan A 8dc6f75f99e4
timesfm-forecasting is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 6d ago), licensed MIT. It adds 73 tokens to every session and 4,201 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to timesfm-forecasting, differing in 0 lines, and is treated as a copy.
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