Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting 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 K-Dense-AI/scientific-agent-skills --skill timesfm-forecastinggit clone --depth 1 https://github.com/K-Dense-AI/scientific-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/k-dense-ai/scientific-agent-skills/timesfm-forecasting)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/timesfm-forecasting"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-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/k-dense-ai/scientific-agent-skills/timesfm-forecasting"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/timesfm-forecasting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 4 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 9d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- timesfm-forecasting — 100% identical, 0 lines differ
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.
- 9d ago First seen · 409 lines · 73 tokens per session scan A 8dc6f75f99e4
timesfm-forecasting is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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