メニュー
Aggregate experiment results from work_dirs into a combined CSV and optionally plot a bubble chart. Use when the user wants to collect, summarize, filter, or visualize benchmark results for a dataset.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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 | aggregate |
| description | Aggregate experiment results from work_dirs into a combined CSV and optionally plot a bubble chart. Use when the user wants to collect, summarize, filter, or visualize benchmark results for a dataset. |
Ask the user:
ETTh1, electricity)pred_len=96, model~Linear, mse<=0.5 — operators =, !=, <, >, <=, >=, ~ substring)# Aggregate (default output: work_dirs/<dataset>/results_all.csv)
uv run python tool/aggregate_results.py --dataset <dataset> [--filter "pred_len=96,model~Linear"]
# One-shot aggregate + bubble chart
uv run python tool/tsf.py aggregate-plot --dataset <name> --pred-len <len>
For a fair leaderboard (TFB-style), add --collapse --aggregate mean --null-threshold 0.3 — collapses per-seed runs into one row per (model, pred_len) and excludes models missing on too many cells (dropped models are logged).
work_dirs/<dataset>/*/performance.csv and profile.csv; profile columns (latency, params, VRAM) require evaluation.enable_profile = true in the run config.--perf-fields, --prof-fields, --metric-cols) supports all recorded metrics: mse, mae, corr, rse, wape, smape, mase.Full flags: docs/en/aggregate-results.md; chart options: docs/en/plot-bubble.md (or the plot skill).