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pyright-typing-fixer

النجوم٠

التفرعات٠

آخر تحديث٩ يونيو ٢٠٢٦ في ١٢:٠٢

Fix basedpyright diagnostics with clean, minimal Python typing changes. Use when the user mentions Pyright, basedpyright, type errors, typing fixes, type checking, or Python typing diagnostics.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

dauvillc

dauvillc/tcfuse

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

SKILL.md

readonly

name	pyright-typing-fixer
description	Fix basedpyright diagnostics with clean, minimal Python typing changes. Use when the user mentions Pyright, basedpyright, type errors, typing fixes, type checking, or Python typing diagnostics.

Content has moved. The full skill documentation is in .agents/pyright-fixer.md. Read that file for the workflow, typing preferences, fix patterns, and output style.

المزيد من هذا المستودع

نفس المستودع

tcfuse-sweep

dauvillc/tcfuse

TC-Fuse hyperparameter search — Hydra Optuna sweeper + submitit launcher running parallel single-GPU SLURM trials (offline-safe, works on Jean-Zay). Search space in conf/hydra/sweeper/*.yaml; launcher SLURM spec in conf/hydra/launcher/jz_*_sweep.yaml; short-budget experiment in conf/experiment/pmw_gmi_sweep.yaml. With submitit=false, scripts/train/train.py returns the best val/loss as the objective Optuna minimizes. Respect the Perceiver's head-divisibility invariants when defining the space. Use when running a sweep, adding a search space, or changing the optimized metric.

2026-06-300

tcfuse-evaluate

dauvillc/tcfuse

TC-Fuse evaluation pipeline — computing metrics and building figures from saved predictions with `scripts/evaluation/evaluate.py` (Hydra, `conf/evaluation.yaml`). Plugin-based: each `Evaluation` (`src/tcfuse/evaluation/`) is enabled via the `conf/evaluation/` config group and writes into its own subfolder under `paths.results/<run_id>/<experiment_name>/`. The base contract imposes no data shape (flattening is plugin-dependent); the `quantitative_metrics` plugin computes RMSE/MAE/R2/MAPE per source/channel with numpy/scikit-learn, independent of torchmetrics. Use when evaluating a prediction run, adding a new evaluation plugin, or changing the results layout.

2026-06-290

tcfuse-inference

dauvillc/tcfuse

TC-Fuse inference and prediction pipeline — running a trained checkpoint over a split with `scripts/inference/infer.py` (Hydra, `conf/inference.yaml`), the single-GPU `trainer.predict` flow with task-specific target masking, the `PredictionWriter` callback, and the `SamplePrediction` / `PredictionRun` output format (per-window HDF5 + `index.parquet` + `manifest.yaml`) with its `compute_metrics` evaluation (bias, RMSE, MAE, R2 per channel). Use when running inference, evaluating a checkpoint, changing the prediction output format, or working in `src/tcfuse/data/predictions/` or `src/tcfuse/lightning/prediction_writer.py`.

2026-06-280

tcfuse-cleps

dauvillc/tcfuse

Drive the CLEPS (Inria Paris) cluster from the local machine — rsync sync, preflight, SLURM submission via submitit, job monitoring, checkpoint resume, and storage quota. Use whenever a task involves `ssh cleps`, $SCRATCH, CLEPS SLURM partitions (cpu_devel / gpu / arches), pixi on the cluster, `submitit`, `setup=cleps_*`, `paths=cleps`, or running jobs on CLEPS. CLEPS uses pixi (no modules) and W&B online (no offline sync), and its scratch is persistent (no archive step).

2026-06-250

tcfuse-architecture

dauvillc/tcfuse

TC-Fuse model backbone design — the architecture-agnostic embedding layer (TorchSource → EmbeddedSource via MultiSourceEncoder) and un-embedding layer (EmbeddedBatch → DecodedBatch via MultiSourceDecoder), the swappable encoder interface, candidate backbones to benchmark (Perceiver, cross-attention Transformer, hierarchical windowed attention), and the masked-source-reconstruction self-supervised pre-training task. Use when working in `src/tcfuse/models/`, adding or benchmarking a backbone, or changing the encoder/decoder interface.

2026-06-230

tcfuse-jz

dauvillc/tcfuse

Drive the Jean-Zay cluster from the local machine — rsync sync, preflight, SLURM submission via submitit, job monitoring, W&B offline sync, checkpoint resume, and storage quota. Use whenever a task involves `ssh jz`, $WORK, $SCRATCH, $STORE, SLURM partitions (gpu_p13 / gpu_p5 / gpu_p6 / cpu / prepost / archive), module loads, `submitit`, `setup=jz_*`, `paths=jz`, `requirements-jz.txt`, or running jobs on Jean-Zay.

2026-06-090