菜单
Plot a bubble chart from an already-aggregated results CSV. Use when the user wants to visualize model performance as a bubble chart and already has a CSV file (e.g. produced by the `aggregate` skill or `tool/aggregate_results.py`).
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | plot |
| description | Plot a bubble chart from an already-aggregated results CSV. Use when the user wants to visualize model performance as a bubble chart and already has a CSV file (e.g. produced by the `aggregate` skill or `tool/aggregate_results.py`). |
The user has an aggregated CSV (typically work_dirs/<dataset>/results_all.csv) and wants a bubble chart. If the CSV does not exist yet, run the aggregate skill first.
Ask for the CSV path and the three axis columns: --x / --y / --size (e.g. mse / mae / total_params).
uv run python tool/plot_bubble.py \
--csv work_dirs/ETTh1/results_all.csv \
--x mse --y mae --size total_params
Output defaults to work_dirs/plots/bubble_<csv-stem>.svg. Scales (--x-scale/--y-scale/--size-scale), grouping (--color-by/--label-by), --title, and --output are tunable — see --help.
tsf aggregate-plot runs aggregation + plotting in one step:
uv run python tool/tsf.py aggregate-plot --dataset <name> --pred-len <len>.
Full flags and behavior (log scales silently drop non-positive rows; bubble sizes normalized to 30–300 pt²): docs/en/plot-bubble.md.
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.