Loading...
Loading...
Found 2,040 Skills
This skill should be used when the user asks to "use marimo", "create a marimo notebook", "debug a marimo notebook", "inspect cells", "understand reactive execution", "fix marimo errors", "convert from jupyter to marimo", or works with marimo reactive Python notebooks.
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
CuTe Python DSL kernel workflow, CuteKernel runtime wrapper, suitability gate, tiling guidance, and CuTe-specific pitfalls. Use when: (1) planning or implementing a kernel in the CuTe Python DSL, (2) the optimization needs more explicit control than cuTile exposes but should remain in a Python-driven workflow, (3) defining package naming for cute-dsl kernels, (4) documenting CuTe Python DSL design choices, (5) recording language-specific knowledge for CuTe Python DSL.
Generate Python code for Naver Open APIs (News Search, Blog Search, Web Search, Datalab Trends, Image Search, Book Search, and other openapi.naver.com APIs). Use this skill whenever the user wants to call Naver APIs, search Naver News/Blog/Web/Images, fetch Datalab keyword trends, or write Python scripts that interact with Naver's Open API platform. This includes requests for code examples, API integration, parameter explanations, endpoint lookup, or troubleshooting Naver Open API calls. Even if the user just mentions 'Naver API', 'Naver news data', 'search trends', or 'openapi.naver.com', activate this skill immediately.
gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.
Manage multiple Alibaba Cloud accounts and batch-export Security Center (SAS) baseline and vulnerability reports via the aliyun CLI and Python scripts. Supports account list refresh, enable/disable, concurrent batch export of cloud platform configuration check (baselineCspm), system baseline risk (exportHcWarning), Linux/Windows/application/emergency vulnerability results across all managed accounts. Use this skill when users need to manage SAS multi-account settings, export baseline or vulnerability compliance data, or merge multi-account security reports into a single file.
Pricing completo de opciones europeas y americanas. 9 metodos: Black-Scholes, Binomial CRR, Trinomial, Monte Carlo (antithetic) + Longstaff-Schwartz, Bjerksund-Stensland 2002 / BAW (American closed-form), Heston 1993 (vol estocastica, sonrisa via Fourier), Bates 1996 (Heston + Merton jumps, crash risk), greeks (BS), implied vol, P(ITM) y P(Profit). Disenado para backtesting: cada funcion es flat Python vectorizado con numpy (sin abstracciones), usa math.erfc (no scipy). BS 2.4 us/op, BS2 3.6 us, Heston 400 us, Binomial N=500 5.6 ms. CLI con 15 modos mas validate y bench. Time complexity O(1) para todos los closed-form.
Guide for using uv, the Python package and project manager. Use this when working with Python projects, scripts, packages, or tools.
Guide for using ruff, the extremely fast Python linter and formatter. Use this when linting, formatting, or fixing Python code.
Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation in FastAPI, Django, and configuration management.
Expert in high-performance CSV processing, parsing, and data cleaning using Python, DuckDB, and command-line tools. Use when working with CSV files, cleaning data, transforming datasets, or processing large tabular data files.
This skill should be used when the user requests to create professional business documents (proposals, business plans, or budgets) from templates. It provides PDF templates and a Python script for generating filled documents from user data.