Create and edit Excel workbooks by running a complete Python script through the shell. Covers multiple sheets, data entry, formatting, formulas, and saving files when other file-reading or code-execution tools fail.
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Extract PDF text when the usual file reader returns errors or unusable output. Choose command-line tools or Python libraries for plain text, metadata, tables, and complex layouts, with checks for scanned or copy-protected documents.
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Run multiline Python code by saving it to a script file before executing it through the shell. The workflow covers working directories, timeouts, error checks, and optional cleanup to make execution easier to debug.
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Apply ReportLab’s built-in paragraph styles to PDF titles, headings, and body text. Learn how to reuse existing styles and create named variations without duplicate style names that can cause KeyError exceptions.
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Continue drafting research-based documents when web search or page reading tools fail. The workflow uses the agent’s existing knowledge, identifies information gaps, and flags claims that need external verification before completing the requested deliverable.
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Continue analysis after repeated tool failures by embedding known data in Python scripts run through run_shell. Save intermediate results as JSON with timestamps and source metadata, creating records that can be checked and reused in later steps.
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Troubleshoot CSV and XLSX processing when execute_code_sandbox fails. The workflow locates input files, saves processing code as a Python script, captures execution errors, checks dependencies and file access, and confirms output exists before reading it.
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Recover from file creation failures in automated tools by creating directories with shell commands and writing files individually. This workflow separates each step so project structures can be created and checked more easily.
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Recover from repeated web search failures by delegating information gathering to shell_agent. Define the required data and output format, allow retries and alternative collection methods, then check source citations and completeness before using the results.
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Recover from repeated code execution tool failures by writing a script through the shell and running it with an absolute path. Check output files and investigate syntax, permissions, disk space, or execution errors when needed.
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Combine video clips first, then add background music or sound effects in a separate pass. The workflow checks source dimensions and duration, uses video copying when appropriate, and separates processing steps to avoid encoder compatibility problems.
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Recover Python file-generation tasks when sandbox execution fails by saving a script and running it through the shell, a command interface. Use the workflow for PDFs, document file operations, dependency checks, and output verification.
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Extract text from existing PDFs and assemble it into a new PDF report using command-line tools. Choose an available conversion method, combine source content, and check the resulting file’s pages and included text.
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Check generated PDFs with PyPDF2, a Python library for reading PDF files. Verify that files exist, contain data, open successfully, and match expected page counts, then use failures to guide regeneration and another verification pass.
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Create PowerPoint presentations with specified slide content and structure. If generation through shell_agent fails, the workflow switches to Python, captures error details, locates the output, and checks the file format, size, and slide count.
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Create PDF reports from existing PDFs using command-line tools. The workflow checks source files, extracts text, organizes report sections, and generates or merges PDFs, with alternative output methods depending on the tools available.
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Continue regulatory research when government pages or PDFs cannot be retrieved. Use alternative sources to draft compliance checklists, clearly distinguish the basis of each finding, and identify requirements that need confirmation from the relevant authority.
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Recover from failed inline Python execution by saving the code as a script, running it through the shell, and checking the output. The four-step workflow helps diagnose errors in multiline code and spreadsheet generation tasks.
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Continue file reading and data processing when read_file or execute_code_sandbox fails. The skill uses run_shell with short Python commands or multiline scripts to inspect files, parse data, and save transformed output.
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Recover failed Python tasks by saving the code as a script and running it through the shell. Provides steps to inspect errors and confirm generated files for spreadsheet creation, data processing, and calculations.
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Create PowerPoint presentations with specified slide content and structure, switching from shell_agent to python-pptx if generation fails. The workflow also locates misplaced files, checks their size and type, and records errors to help diagnose problems.
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Helps agents build outputs from supplied reference files by reading, checking, and citing their data first. External searches are reserved for missing details, verification, or tasks that explicitly require current information.
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Continue library-dependent Python tasks when sandbox execution fails by switching to a shell script. Covers recognizing import errors, preserving script text, checking execution output, and troubleshooting package availability for tasks such as PDF generation or visualization.
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Extract text from PDFs when read_file fails or returns unreadable data. Use dedicated command-line tools or Python libraries to retrieve text and tables, preserve spacing where possible, inspect document properties, and validate the extracted content.
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