Use when working in a Unity project that uses Entities (DOTS) or when a task needs thousands of simulated objects - when ECS is worth it, components/bakers/ISystem with IJobEntity and Burst, entity command buffers for structural changes, native memory rules, and testing systems in a test World.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when starting any game task, before writing tests - find out which editors and runners exist on this machine, keep gameplay rules in engine-independent classes with an injected clock and seeded RNG, and report exactly what could not run.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when touching Unity per-frame code, spawning, physics queries, UI updates or rendering setup - profiling with numbers, zero-GC hot paths (caching, NonAlloc queries, no LINQ/closures/boxing, TMP SetText), ObjectPool, allocation tests, Burst/Jobs for bulk math, and rendering/physics settings that cost frames.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the repository is Defold, MonoGame, pygame/pygame-ce, Excalibur or KAPLAY (or melonJS, Stride, Python arcade, macroquad/ggez) - each engine's lifecycle and delta, where logic goes, the test runner, the headless build/run command, and how to capture a frame.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when writing or restructuring Unity C# code - assembly definitions and dependency direction, small components with cached references, ScriptableObjects for data and events, DI instead of singletons, Input System, Addressables, serialization safety and Unity 6 API changes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when writing or running Unreal Engine tests - which test type fits (simple/complex automation tests, Automation Specs, functional tests, CQTest, Low-Level Tests, Gauntlet), naming and filters, latent tests, and the headless UnrealEditor-Cmd run with report export, exit handling and screenshot captures.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when training, tuning, comparing or evaluating a model — choosing the cross-validation scheme, baselines, tuning inside CV, thresholds and calibration, per-slice metrics, feature importance and error analysis
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when writing or changing analytical SQL or a dbt model — CTE structure, window functions, grain and join correctness, dbt data tests, unit tests, contracts and incremental models, and how to run them
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when building or changing features, preprocessing, encoders, resampling or train/validation/test splits — keep every fitted transform inside a Pipeline fitted on training folds only, and build time and group features without seeing the future or the same entity twice
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a change trains a model, reports a metric, or touches seeds, data or model versions, the lockfile, MLflow or DVC — what every run must log, seeding, environment pinning, data versioning and keeping tests off the shared tracking server
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a task asks for a model, a score, a forecast or a prediction-driven feature — pin down the decision, target, unit, prediction time, metric and baseline before writing any modelling code
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a task touches a Jupyter or marimo notebook, asks to productionise or schedule notebook logic, or when you are tempted to do the work in a notebook — extract logic into tested modules, keep the notebook thin and reproducible, and commit it the way the repo does
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when dataframe code is slow or runs out of memory, when data outgrows pandas, or when writing pandas 3, Polars or DuckDB code — vectorisation, dtypes, Copy-on-Write, lazy and out-of-core execution, and measuring before and after
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when adding or changing a scheduled data or ML pipeline — Dagster assets, Prefect flows, Airflow DAGs or a cron'd entrypoint — idempotent partitioned stages, retries, backfills, quality gates, and testing the pipeline without the scheduler
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a model is served online (FastAPI, BentoML) or scored in batch, when changing how a model artifact is loaded or versioned, or when adding drift, quality or prediction monitoring and a rollout plan for a new model
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when starting a Python data or ML project, adding its first tooling, or changing pyproject.toml, the lockfile, the package layout or the lint/type/test configuration — detect the repo's tooling first, then uv, ruff, mypy, pytest and a src layout
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when writing or changing PyTorch code — model, Dataset/DataLoader, training and evaluation loops, determinism, mixed precision, torch.compile, checkpoints with safetensors, Hugging Face models, ONNX export, and tests that run on CPU
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when tools from the jupyter, duckdb, dbt, mlflow, huggingface or postgres MCP servers appear in your tool list, or before reaching for one — what each is good for, what it can damage, and the CLI fallback when it is not connected
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you meet a new dataset, table, file or source, before building features or a model on it, or when a number looks wrong — profile grain, keys, nulls, cardinality, time coverage and target balance, and persist what you learn as code
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when writing tests for data or ML code — what to test at each layer (transforms, schemas, properties, pipeline smoke, model-quality thresholds, determinism and parity), how to build small synthetic fixtures, and what not to test
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you are about to hand off any number, table, chart or analysis someone will act on (a metric query, a dbt mart, a report, an experiment readout) — the pre-delivery checklist for data quality, calculation, reasonableness and presentation, and the pitfalls that produce plausible wrong numbers
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing or analysing an A/B test, computing power or sample size, testing whether a difference is real, or writing any statistical claim — effect sizes with intervals, SRM checks, multiple comparisons and causal caveats
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when data crosses a boundary into your code (file, API, warehouse table, upload, model input or output), when a join or cleaning step can change row counts, or when adding pandera, Great Expectations, Pydantic or dbt checks
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when an approved analiz spans more than one repository - split the work per repository and open one or more tasks per repository, each carrying its own plan slice
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.