القائمة
Visualize dataset samples from a TOML config in the ModernTSF project. Use when the user wants to plot, inspect, or preview time-series samples from a dataset split.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Add a new model to ModernTSF by scaffolding it with `tool/tsf.py new-model`, filling the architecture, and verifying with `tsf smoke`. Use when the user wants to integrate a new PyTorch model, register a custom architecture, or wire a new forecaster into the benchmark pipeline.
Verify model(s) end-to-end by running their smoke configs concurrently and reporting PASS/FAIL via `tool/tsf.py smoke`. Use when the user wants to check that a model (new or existing) trains and produces the right output shape, validate a port, or run a quick CI-style gate over many models.
Understand a model in ModernTSF by reading its README card as progressive disclosure — paper venue/date/arXiv/abstract first, then source only if needed. Use when the user asks "what is model X", "tell me about X", "了解 X 模型", "how does X work", which paper is X from, or wants to compare/learn what a forecaster does before running it.
Add or run a *probabilistic* forecasting model in ModernTSF — one that outputs quantiles or a distribution (prediction intervals / uncertainty) instead of a single point, and is scored with CRPS / WQL / coverage. Use this whenever the task involves prediction intervals, quantiles, uncertainty, a probabilistic or distributional forecast, pinball / quantile loss, Gaussian NLL, or the metrics `crps` / `wql` / `coverage_80` / `width_80` — even if the user only says "predict a range" or "give me uncertainty bands". Do NOT use the plain `add-model` flow for these: it builds a point model. Probabilistic output is a separate `output_type` axis, orthogonal to `task.mode`.
Package a finished run into a TSEval Submission Report (result + agent trajectory + report) and contribute it via a GitHub PR to the TSEval leaderboard repo, via `tsf trace` / `tsf submit`. Use when the user wants to submit results to the TSEval leaderboard, capture an experiment trajectory, or build a leaderboard from submissions.
Diagnose and report a ModernTSF framework defect upstream, with user approval, as a GitHub issue or a small verified PR against Diaugeia/ModernTSF. Use after reproducing a crash in src/ or tool/, wrong output or shapes, a broken config or registry, a doc/CLI mismatch, or another repository defect rather than a problem in the user's data or code.
| name | visualize |
| description | Visualize dataset samples from a TOML config in the ModernTSF project. Use when the user wants to plot, inspect, or preview time-series samples from a dataset split. |
Plots raw dataset samples (input + forecast window) from a config. For forecast-vs-truth case plots from a trained model, use the experiments skill instead.
Ask for:
configs/datasets/etth1.toml) — any dataset-only or full run TOML workstrain, val, or test (default train)--num-samples, default 3) or a specific --indexuv run python tool/visual_data.py \
--config configs/datasets/etth1.toml --split train --num-samples 3
Saves to work_dirs/plots/<dataset>_<split>.png by default (input series solid, forecast window after the dashed line). Other knobs: --channels 0,1,2 (default all), --seed S, --save PATH, --show.
Full flags: docs/en/visualize-data.md. Dataset configs live in configs/datasets/.