331 skills · sorted by relevance
Locate missing files and verify their full paths before processing them. This workflow checks directory contents, searches by filename, and inspects permissions, size, and modification time to help resolve FileNotFoundError, an error indicating a file could not be found.
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.
Trace uploaded files through extraction, conversion, parsing, and reconstruction of saved objects in a CTF sandbox, an isolated competition environment. Preserve original and derived files, distinguish visible validation from backend behavior, and document the smallest reproducible processing chain.
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.
Assess file upload and import features for weaknesses in file checks and processing. The skill covers extension and content-type bypasses, unsafe image and document handling, archive extraction flaws, and evidence needed to confirm execution or browser-side impact.
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.
Extract readable text from PDFs with pdftotext when read_file returns garbled or binary content. Choose page ranges, preserve text layout where needed, and send the extracted content to standard output or a text file for further processing.
Investigate file inclusion and path traversal flaws that let applications access unintended files. The skill guides checks for sensitive data exposure and possible code execution, using content evidence or repeatable indirect signals to avoid false positives.
Design caching for repeated PDF parsing, text extraction, or image analysis using SHA-256 hashes, identifiers calculated from file contents. Reuse results after files move or change names, and process them again when their contents change.
Coordinate changes to shared task folders with .processing marker files that indicate ongoing work. Managers and Workers check these markers before editing, synchronize files through MinIO, and remove markers after completing their changes.
Inspect scientific data files to understand their format, structure, and quality. Covering over 200 formats, this skill produces Markdown reports with statistical summaries, potential issues, and recommendations for preprocessing, visualization, and further analysis.
Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.
Check CPU cores, GPUs, available memory, and disk space before demanding computing tasks. Save a JSON report with recommendations for parallel workers, GPU use, and processing data in chunks when memory is limited.
Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.
Create, debug, and maintain Windows batch scripts for file operations, backups, and scheduled tasks. Covers command arguments, variables, loops, and error handling, with starter templates for command-line tools and reusable functions.
Prepare shared instructions for translating, rewriting, or extracting information from many independent files, then submit a plan through the DashScope Batch API. Processing runs asynchronously at half the realtime list price and delivers new files later.
Continue file reading and data processing when execute_code_sandbox or read_file fails. This skill runs multiline Python through run_shell, with examples for text, JSON, spreadsheets, PDF and Word documents, plus basic summaries and grouped calculations.
Collect posts from X search, profile, individual post, and list URLs. Organize text, authors, engagement counts, media, hashtags, and mentions into a consistent dataset, with support for collecting additional pages.
Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory. Use when a user or agent mentions large files, big data, out-of-memory errors, OOM, scaling up, tall arrays, datastores, or needs to process multiple tabular files. Covers the decision between datastore + tall, datastore + transform, and parallel execution. Also use when a user or agent has working in-memory code (readtable, parquetread) that runs out of memory and needs a migration path. Also covers building custom datastore classes for proprietary or non-standard formats — use when the task requires subclassing matlab.io.Datastore, implementing a custom reader, building an extensible datastore, or integrating a new file format with tall arrays or parallel computing. Do NOT use for MAT files (use matfile instead).
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Manage files in Box, organize folders, share content, and search documents or metadata. Use Box AI to summarize files, answer questions, and extract information, with guidance for bulk processing and building applications connected to Box.
Submit groups of AI requests as batch jobs with the OmniRoute command line. Manage input files, monitor or cancel jobs, save results and errors, and incorporate batch processing into automated build and deployment workflows.
Look up Google user profiles and search the Workspace directory through gog. Retrieve your own profile, a profile by ID, or user relationship information, with JSON output available for further processing.
Recover failed Python tasks by saving the code as a script and running it through the system shell. The workflow covers spreadsheet generation, data processing, output verification, and troubleshooting missing packages, paths, or permissions.
Continue Python tasks when sandbox execution fails by saving code as a script and running it through run_shell. The workflow covers dependencies, execution checks, and output verification for PDFs, image processing, data analysis, and file operations.