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 mxslr/mlcraft --skill domain-time-seriesgit clone --depth 1 https://github.com/mxslr/mlcraftWrote 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/mxslr/mlcraft/domain-time-series)<a href="https://agentmods.dev/skills/mxslr/mlcraft/domain-time-series"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-time-series.svg" alt="Measured on agentmods" 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 | $0.00101 | $0.00585 |
| Opus 5 | $0.00051 | $0.00293 |
| Sonnet 5 | $0.00020 | $0.00117 |
| Haiku 4.5 | $0.00010 | $0.00059 |
Grade A, and why
domain-time-series 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 8d 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.
What it actually says
Time-Series - Method Selection
Temporal order is sacred: the future must never leak into training.
Decision table
| Sub-task | Recommended (simple to advanced) | Notes |
|---|---|---|
| Univariate / few series forecasting | ETS / ARIMA / Theta / Prophet | Strong, interpretable baselines. Often beat deep on small data. |
| Many related series | LightGBM on lag/calendar features then N-BEATS/NHITS, TFT | GBT-on-lags is a very strong, cheap default. |
| Long-horizon / rich covariates | TFT / PatchTST / DeepAR | Deep only when data volume justifies it. |
| Foundation / zero-shot | TimesFM / Chronos / Moirai | Good for cold-start / many series with little history. |
| TS classification | ROCKET/MiniROCKET, InceptionTime, HIVE-COTE | ROCKET = fast strong baseline. |
| Anomaly detection | STL/residual + threshold, Isolation Forest, autoencoder/LSTM | Start simple; label scarcity is the norm. |
Temporal rigor (non-negotiable)
- Split by time: train strictly before validation before test. Use rolling/expanding-window backtesting, not random K-fold (
data-rigor-and-leakage). - No future leakage in features: lags/rolling stats use only past data; align target horizon carefully; no target-derived or post-hoc features.
- Fit scalers/encoders on the training window only, refit as the window rolls.
- Respect gaps/frequency: handle missing timestamps, irregular sampling, and known future covariates (holidays) vs unknown.
Evaluation
- Metrics: MAE / RMSE / MAPE / sMAPE / MASE (MASE compares to a naive baseline - always include a naive/seasonal-naive baseline).
- Report per-horizon error and prediction intervals (coverage), not just a point metric.
- Backtest across multiple origins; a single split is not enough.
- Improve: use
accuracy-improvement-loop(better features/lags, hierarchical reconciliation, ensembling classical+ML, then deep).
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.
- 8d ago First seen · 31 lines · 101 tokens per session scan A 5c04348fcdae
domain-time-series is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 2mo ago), licensed MIT. It adds 101 tokens to every session and 585 once invoked, about $0.0005 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 2026-08-31.
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