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Write large text files when write_file or shell_agent fails because of content size limits. The fallback passes multiline content through run_shell to Python, with guidance on escaping characters and checking the saved file.
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Save large text files when write_file or shell_agent encounters payload size limits. Create the parent directory first, write the content with Python through run_shell, and verify the resulting file.
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Run Python code directly on the host when repeated e2b sandbox failures block execution. The skill explains saving scripts, launching them through the shell, and checking outputs when an isolated environment is unnecessary.
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Move Python execution to saved script files when execute_code_sandbox repeatedly fails or becomes unstable. The skill covers writing single or multiple files, running them through run_shell, inspecting outputs and errors, and removing temporary scripts afterward.
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Read, write, and transform workspace files by running Python through run_shell when execute_code_sandbox cannot access them. The workflow covers explicit file paths, batch processing, error handling, and checking generated outputs.
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Inspect PowerPoint files when standard readers fail by using python-pptx through the shell. Produce structured results showing slide counts, titles, and shape counts to check presentation structure before delivery.
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Inspect PowerPoint files with python-pptx when standard readers fail or return incomplete information. Retrieve slide counts and titles, compare them with presentation requirements, and report missing or problematic content without installing PowerPoint.
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Debug PowerPoint generation with python-pptx, a Python library for presentations. Follow a sequence of installation checks, minimal tests, error logging, and fixes for tables and charts, then inspect the generated slides.
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Generate PDF documents when sandbox execution fails with unclear errors. Check available Python libraries, switch to direct shell execution, and verify the resulting file while collecting error details to help diagnose problems.
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Extract text, tables, and headings from Word documents when standard file reading tools fail. The workflow uses Python and python-docx through run_shell to recover document content for further review or analysis.
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Extract text from Word DOCX files with shell commands when python-docx is unavailable. The workflow reads the document’s XML content, removes tags, optionally cleans whitespace, and saves plain text for inspection or further use.
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Create, edit, or validate Jupyter notebooks for SQL and Python analysis. Organize data sources, assumptions, calculations, and findings into a readable document that others can review and rerun, with execution gaps clearly identified.
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Check the SenseNova-Skills installation, Python dependencies, and environment variables before using other skills. Prompt for missing configuration, save supplied values to a .env file, and reload settings or suggest restarting the agent.
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Build MCP servers, which connect AI agents to external services, using TypeScript or Python. Plan API access, design understandable tools and structured responses, implement authentication and error handling, and check behavior with the intended client.
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Use SymPy in Python to solve equations, simplify formulas, calculate derivatives and integrals, and work with matrices. Keep exact results such as fractions and square roots, then turn expressions into executable code or LaTeX.
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Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.
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Organize research code, datasets, and outputs with consistent folders and script names. Provides Python and Stata templates, processing stage numbers, and guidance for paths, logs, and documentation so collaborators can follow the workflow.
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Build causal inference analyses to estimate intervention effects using templates drawn from Causal Inference: The Mixtape. Adapt code across Python, R, and Stata, with checks for method assumptions and guidance on language-specific packages and limitations.
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Use this before writing Python code, creating Python scripts, modifying Python files, or when user asks to "write python", "create a python script", "implement in python". TRIGGER when starting any Python development task.
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高速Python依存関係管理、仮想環境、モダンなPythonプロジェクトワークフローのためのuvパッケージマネージャーをマスター。Pythonプロジェクトのセットアップ、依存関係の管理、uvによるPython開発ワークフローの最適化時に使用。
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在Hugging Face Hub查找可在本机运行的语言模型，比较GGUF文件及容量，选择适合内存的版本，并生成llama.cpp启动命令。
用Python实现文字生成图像、现有图像风格转换和指定区域补全，并指导如何控制构图与风格、调整生成参数及减少内存占用。
Check physical units, propagate measurement uncertainty through calculations, and format results with meaningful precision. Local tools also inspect Python code for unit and uncertainty mistakes and assess whether calculated magnitudes are physically plausible.
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Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.
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