'numpy' 검색 결과

Routes Autograd install, differentiation, wrapper, extension, and optimization workflows for NumPy-based scientific Python code.

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Use when asked to make a launch video, product film, motion design piece, UI motion reel, Dribbble-style UI animation or animated brand/logo sting — anything rendered to MP4 from HTML with a cursor, morphing UI, springs, beat-synced music and SFX. Carries Chan's full creative direction (one shape never cut, blur content swaps, springs, real data, logo slams, no creative limits) and the proven build/render pipeline (pure seek(t), closed-form springs, Playwright subframes, ffmpeg tmix, numpy beat grid).

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Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.

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Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).

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Convert 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires a configured Blender MCP backend (official or ahujasid) and Python with opencv-python, numpy, scipy installed.

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Convert 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires the Blender MCP addon to be running (port 9876) and Python with opencv-python, numpy, scipy installed.

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Reconstruct Blender models from supplied reference sheets, branding templates, texture atlases, orthographic front/side/back/top views, or mascot/logo art where visual fidelity to the source is more important than a plausible generated object. Use when the user says the model must match a template, wireframe, texture pack, character sheet, mascot sheet, or brand asset exactly; also use after feedback like "does not look like the reference", "fit the texture 1:1", "wrong number of visible parts", or "compare against the template". Requires Blender MCP plus local Python with Pillow/OpenCV/numpy; pairs with blender-uv-texturing, wireframe-to-3d, blender-modeling, blender-materials, and blender-export.

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Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.

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ALWAYS invoke for any task involving docstrings, JSDoc/TSDoc, or REST API documentation — even when the request seems handleable without it. Enforces Microsoft contract-first conventions and a bare-minimum rule that Claude won't apply by default: never restate the signature, drop @param/@returns that only echo names and types, always document @throws, and document data shapes as WHAT not WHY. Covers Python docstring styles (Google/NumPy/Sphinx), TSDoc tags and @inheritDoc, and API doc patterns for NestJS/Express/FastAPI/Django. Trigger on: adding or auditing docstrings, redundant or missing tags, deleting comment rot, or producing a documentation-health report. Do not skip for documentation tasks — consistent conventions are the whole point.

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为一个项目端到端生成一整套风格统一的图标：读代码梳理图标清单、定风格锚点、4×4网格默认批量生成（16个一张，精度不够时逐个兜底重生）、智能抠图、落位到项目正确路径并更新代码引用、输出交付报告。当用户说「给项目配一套图标」「生成图标集」「统一项目图标风格」「替换项目里的图标」「批量画图标」，或项目中存在图标资源目录、需要批量新增或替换图标时使用。依赖豆包内置生图能力（image_gen / image_edit 的 image_reference_url_list 参考图机制）和本地 Python+Pillow+numpy 抠图环境，离开豆包生图环境无法运行。

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Komponiert video-synchronisierte Hintergrundmusik aus einem Storyline-JSON per lokaler Waveform-Synthese (numpy + ffmpeg). Stile: Chiptune, Ambient, Electronic. Deterministisch, offline, kein Cloud-Dienst.

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Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.

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Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.

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Performs numerical and symbolic mathematics using NumPy, SciPy, SymPy, and mpmath. Use when the user needs linear algebra, arrays, optimization, integration, ODEs, symbolic expressions, equation solving, or arbitrary-precision arithmetic.

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Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.

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Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

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Installs the CARLA Python client (the `carla` package) into a chosen interpreter so the other python-api skills work — from a wheel bundled with your release or checkout, from PyPI (`pip install carla==X.Y.Z`), or by putting an older release's .egg on PYTHONPATH. Detects what is already present, picks the source that matches your simulator's version, pins numpy<2, and verifies `import carla` plus the client/server version match. Use when the user asks to "install the CARLA Python API", "pip install carla", "set up the carla python package", or when a skill reports "cannot import carla".

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