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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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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Extract text from PDFs by trying several methods in sequence and checking the results. When ordinary extraction yields too little text, switch to OCR, which recognizes text in images, to process scanned documents.
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Provides fallback methods for extracting PDF text when the usual file reader fails or returns incomplete content. Uses command-line tools or Python libraries to retrieve text, preserve spacing, inspect metadata, and extract tables.
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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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Diagnose Python script failures by showing error output and exit codes alongside the working directory and file listing. Use these clues to investigate missing files, missing modules, permission errors, or scripts that produce no output.
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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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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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Diagnose Python spreadsheet failures through staged checks of the interpreter, library imports, file access, and basic operations. Minimal test scripts help separate environment problems from errors in the full processing script.
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Build answers and reports from provided reference files before searching the web. The workflow identifies attachments, extracts and checks relevant data, tries alternatives for unreadable Word files, and states sources and missing information.
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Build complex Excel files in four stages: extraction, preparation, calculation, and formatted output. Intermediate CSV files let agents and reviewers inspect data, validate forecasts, locate errors, and rerun individual stages without repeating the entire workflow.
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Use shell heredocs, which pass multiline text directly to a command, when write_file fails on complex code. Save TypeScript, JSX, JSON, or shell scripts with literal quotes and formatting, then verify the file and its contents.
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Check which H.264 video encoders are available before writing FFmpeg scripts. Choose an available encoder or copy an existing video stream when appropriate, then test a short clip to catch compatibility problems before processing the full file.
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Extract readable text from PDFs when normal file reading returns garbled or unusable content. The workflow switches between extraction tools, checks the results, and includes optical character recognition for scanned pages when needed.
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Extract text from local PDF files with pdftotext or PyMuPDF, a Python library for reading PDFs. Convert individual files or batches into text files that an agent can read for summarization, analysis, or further data extraction.
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Organizes creation of a spreadsheet, a diagram, and a summary PDF as a related report package. Allocates attempts across components, checks generated files, and provides fallback methods when a tool or output fails.
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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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Recover document and PDF generation tasks when sandbox errors provide little detail. Checks Python libraries, runs generation through the shell, captures errors, and verifies output files, with alternative PDF tools and text output as fallback options.
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Extract PDF text using a sequence of tools when one method fails. Validate downloaded files, check whether the extracted text is substantial, record the successful method, and investigate alternative sources if extraction remains unsuccessful.
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Recover from Pandoc PDF generation failures by checking installed PDF engines and retrying alternatives. The guide matches engines to Unicode text, simple Latin documents, or HTML-based layouts, and includes an automated fallback script.
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Retrieve PDFs from web sources and extract their text through an ordered sequence: direct reading, pdftotext, then PyMuPDF. Includes file validation, extraction checks, and guidance for documenting inaccessible sources and distinguishing supplementary content.
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Extract text from PDFs by checking each tool’s output and switching methods when needed. Guides agents from file reading to pdftotext and PyMuPDF, then checks readability, missing sections, and records extraction failures.
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