'python' 검색 결과

Turn a Python script into a beginner-friendly project by improving its structure and naming, adding comments that explain the reasoning, and creating a README.md tutorial covering setup, code logic, and example usage.

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• Turning a Python script into learning material
• Explaining code logic to beginners
• Documenting setup steps and example usage

Set up cognee and run a first Python example that stores information and retrieves it with a question. Covers virtual environments, API key configuration, optional packages, and basic troubleshooting.

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• Installing cognee in a new Python environment
• Trying a minimal example that stores and retrieves information
• Separating stored information by project

Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.

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• Standardizing instrument data for a laboratory management system
• Converting ELISA results into an importable CSV
• Exporting Python code to parse NanoDrop files

Measure Python SDK test coverage, the code exercised by tests, and identify meaningful gaps in user-visible behavior. Report proposed scenarios or add authorized tests, then verify the changes and report remaining gaps alongside coverage results.

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• Auditing Python SDK test coverage
• Investigating a decline in coverage metrics
• Finding untested error and cancellation paths

Check Agent Skills for documentation structure and Python script behavior, then score quality and security posture. Get grades and improvement priorities, and use pass criteria in pre-commit checks or continuous integration, which runs checks automatically.

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• Checking a newly authored skill before release
• Auditing existing skills and prioritizing improvements
• Testing bundled Python scripts and sample outputs

Build AI applications in Python with Genkit, including conversational agents, generation workflows, streaming responses, and tool use. The skill also guides setup and debugging through traces, recorded details of model inputs, outputs, and actions.

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• Build a chatbot that keeps conversation history
• Implement streaming responses and tool calls
• Investigate Genkit import and API errors

Guide shell, Python, and Node.js execution on the computer running a connected A0 CLI. It explains session reuse, interactive input, output retrieval, and recovery steps when execution permissions, timeouts, or disconnections interrupt the work.

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• Run shell commands on the connected A0 CLI host
• Execute Python or Node.js on the host
• Retrieve output or answer an interactive program

Install Scenic, a language for describing scenarios with randomized conditions, into the Python environment used by CARLA. Check command-line tools and version compatibility, optionally download example scenarios and map assets, and save the installation paths.

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• Preparing Scenic to work with CARLA
• Diagnosing scenario model import failures
• Checking missing vehicle definition tables

Build Python APIs with FastAPI and Pydantic V2, including input validation, database operations that avoid blocking other work, and token-based authentication. Implement REST or WebSocket endpoints, then check their behavior with automated tests and generated API documentation.

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• Build REST APIs with FastAPI
• Validate input data with Pydantic V2
• Implement JWT authentication and authorization

Use persistent virtual terminal sessions to keep directories and environment variables across commands, interact with Python or database consoles, and answer prompts. The skill explains sending input, reading responses, detecting prompts, and closing sessions when finished.

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• Keep directories and environment variables across commands
• Run a sequence of commands in an interactive Python console
• Interact with mysql or psql consoles

Inspect imports and agent patterns in existing Python code to identify its framework and recommend an Omnigent executor, the component that runs the agent. Explain unsupported frameworks and suggest available next steps.

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• Identify the framework used by existing Python agent code
• Choose an executor for Claude SDK or OpenAI Agents SDK code
• Check whether a framework has native support

Continue Python tasks when the sandbox cannot start by switching to run_shell. The workflow covers short scripts, saved script files, library checks, longer timeouts, and output verification for tasks such as PDF generation and data processing.

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• Continue Python work after sandbox startup failures
• Generate PDFs through shell execution
• Run data processing with required libraries

Resume Python tasks after execute_code_sandbox errors or timeouts by saving the attempted code as a script and running it through run_shell. The workflow includes dependency checks, execution timeouts, and verification of generated files such as Excel reports.

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• Retrying Python code after sandbox errors
• Resuming Excel or CSV generation after a failure
• Running saved scripts with a longer timeout

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.

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• Continuing Python tasks after sandbox initialization failures
• Generating PDFs from a saved script
• Running image processing or data analysis through the shell

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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• Retry failed Excel or CSV generation
• Resume data transformations after a sandbox error
• Investigate Python errors through shell output

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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• Running multiline Python scripts
• Handling scripts with quotes and special characters
• Investigating failed heredoc execution

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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• Reading accessible files after read_file errors
• Inspecting JSON settings through Python
• Checking CSV rows after sandbox execution fails

Recover PDF or DOCX generation when execute_code_sandbox fails with unclear errors. Use run_shell to execute Python directly, check required libraries, report specific failures, and verify that the output file was created.

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• Retry PDF generation after an unclear sandbox error
• Generate DOCX files by running Python through the shell
• Check and install missing document libraries

When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.

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• Retry Python work through the shell after repeated sandbox failures
• Run longer tasks with an adjusted timeout
• Execute Python work that needs multiple files or system utilities

Debug repeated execution failures by saving Python code to a script file and running it through run_shell. Keep the script available for inspection, review intermediate output and errors, and edit it for another run.

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• Investigate repeated agent or sandbox execution failures
• Trace data processing with intermediate output
• Edit and rerun a saved Python script

Prepare a Python environment for Mixpanel analysis by installing required libraries and checking credentials and connectivity. Use it for first-time setup, missing dependencies, or guidance on service account and OAuth sign-in options.

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• Set up a new environment for Mixpanel analysis
• Install missing Python analysis libraries
• Configure service account or OAuth credentials

Review Python changes against shared team standards to find bugs, missing regression tests, and security issues. Findings are ordered by severity and include file names and line numbers so reviewers can locate each problem.

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• Reviewing Python changes before merging
• Checking that bug fixes include regression tests
• Finding unsafe SQL and exposed secrets

Deploy n8n on a Linux server using Docker Compose and Caddy for automatic HTTPS. Choose a single instance or queue workers, configure secrets and Python task runners, and follow procedures for updates, backups, and recovery.

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• Deploy n8n on a personal VPS with HTTPS
• Set up queue workers for parallel workflow execution
• Update, back up, or restore an n8n instance

Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.

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• Explore distributions, missing values, and relationships
• Run regressions on cross-sectional or panel data
• Export publication-ready regression tables and figures