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 vasilyu1983/AI-Agents-public --skill ai-ml-timeseriesgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-ml-timeseries)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-ml-timeseries"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-ml-timeseries/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/vasilyu1983/ai-agents-public/ai-ml-timeseries"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-ml-timeseries.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00031 | $0.03625 |
| Opus 5 | $0.00015 | $0.01813 |
| Sonnet 5 | $0.00006 | $0.00725 |
| Haiku 4.5 | $0.00003 | $0.00363 |
Grade A, and why
ai-ml-timeseries 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.
How it starts
The opening of the file, as written. The whole thing — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Time Series Forecasting - Production Patterns
Scope note: This skill covers forecasting system construction and evaluation. It is not part of the LLM-build or LLM-training stack — route LLM lifecycle, prompting, or provider questions to ai-llm.
July 2026 posture: define a cutoff timestamp before modelling, start with strong baselines, prefer horizon-aware validation over IID thinking, treat known-future covariates explicitly, and verify fast-moving tooling against current official docs before recommending it.
This skill is the implementation guide for forecasting systems:
- timestamp integrity, frequency checks, and point-in-time feature design
- local, global/panel, and hierarchical forecasting workflows
- leakage-safe backtesting, horizon-wise evaluation, and business-loss alignment
- probabilistic forecasting, calibration, and interval quality
- time-series foundation models (TSFMs) and zero-shot benchmark patterns
- forecasting-specific handoff, fallback, and lineage requirements
Use this skill for forecasting depth. Use sibling skills for general data science, generic LLM strategy, or full production operations.
When To Use This Skill
Activate this skill when the user asks for:
- building or reviewing a forecast model
- choosing between local, global/panel, hierarchical, or foundation-model approaches
- creating lag, rolling, calendar, or known-future covariate features
- designing a rolling-origin backtest or fixing temporal leakage
- selecting forecasting metrics by horizon, segment, or business cost
- adding prediction intervals, quantiles, or conformal calibration
- comparing Chronos-2, Chronos-Bolt, Toto, TimesFM, AutoGluon TimeSeries, MLForecast, skforecast, or classical baselines
- defining forecast-specific fallback, lineage, and handoff requirements
Scope Boundaries
- General EDA, tabular modelling, experiment design, or reusable DS workflow -> ai-ml-data-science
- Deployment architecture, monitoring stack, release gates, incident playbooks -> ai-mlops
- Generic LLM lifecycle, prompting, or provider selection -> ai-llm
- RAG and search systems -> ai-rag
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.
- agents/openai.yaml 376 B
- assets/timeseries/template-backtest.md 1005 B
- assets/timeseries/template-calendar-features.md 565 B
- assets/timeseries/template-forecast-model.md 1.2 KB
- assets/timeseries/template-lag-rolling.md 939 B
- assets/timeseries/template-multistep-strategy.md 546 B
- assets/timeseries/template-resample-fill.md 737 B
- assets/timeseries/template-ts-eda.md 848 B
- assets/timeseries/template-ts-llm.md 934 B
- assets/timeseries/template-ts-metrics.md 709 B
- data/sample-forecast-results.json 13 KB
- data/sources.json 8.5 KB
- learnings.consolidated.md 592 B
- learnings.md 403 B
- references/anomaly-detection-patterns.md 13 KB
- references/backtesting-patterns.md 7.4 KB
- references/forecast-governance-patterns.md 2.0 KB
- references/global-panel-forecasting-patterns.md 3.0 KB
- references/hierarchical-forecasting.md 18 KB
- references/intermittent-demand-patterns.md 14 KB
- references/lag-rolling-patterns.md 1.5 KB
- references/lightgbm-ts-patterns.md 7.0 KB
- references/model-selection-guide.md 7.0 KB
- references/multistep-forecasting-patterns.md 8.1 KB
- references/probabilistic-forecasting.md 8.6 KB
- references/production-deployment-patterns.md 17 KB
- references/ts-eda-best-practices.md 1.7 KB
- references/ts-llm-patterns.md 12 KB
- scripts/README.md 3.8 KB
- scripts/ts_evaluator.py 22 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 · 290 lines · 31 tokens per session scan A a0a82368c52f
ai-ml-timeseries is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 31 tokens to every session and 3,625 once invoked, about $0.0002 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 2026-08-30.
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