개발 스킬

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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• Investigating Python failures during automated execution
• Checking file paths after a FileNotFoundError
• Diagnosing Python failures in CI/CD pipelines

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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• Retrying Python code after missing-library errors
• Continuing PDF generation through shell execution
• Running visualization code with installed packages

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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• Run scripts when automated execution tools keep failing
• Save multiline scripts without shell variable expansion
• Check generated files for size and type

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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• Saving TypeScript or JSX files after repeated write_file failures
• Writing JSON files containing quotes and special characters
• Preserving variables and line breaks in shell scripts

Inspect website responses with curl, using browser-like request headers and checking file size, content, and format. Identify placeholder pages and choose a browser-based or direct API approach when the expected content is missing.

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• Investigating downloads that contain no page content
• Checking whether a response is HTML, JSON, or PDF
• Fetching pages that vary by browser identification

Investigate unclear errors in Python PDF generation by displaying output immediately and inspecting error details. Use the resulting traceback, a record of where execution failed, to identify missing dependencies, font problems, or invalid file paths.

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• Investigating unknown errors when saving PDFs
• Checking missing fonts and dependencies
• Diagnosing incomplete PDFs or silent crashes

Package project files into a ZIP archive and verify that the deliverable exists and is not empty before completing a task. It also defines a fallback for archiving available files when the agent approaches its iteration limit.

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• Delivering a code project as a single ZIP file
• Checking that a delivery archive exists and is not empty
• Reviewing files before packaging a project

Recover from repeated sandbox execution failures by saving Python code to a script and running it through run_shell. Check output, error messages, and exit codes to confirm results or diagnose remaining execution problems.

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• Continuing Python work after two consecutive sandbox failures
• Switching execution methods after persistent timeouts
• Diagnosing script failures through error output

Continue Python tasks after repeated sandbox failures by saving code as a script and running it through the shell, a command interface. The workflow covers execution checks, result retrieval, and optional cleanup.

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• Continuing data processing after repeated sandbox errors
• Saving execution results to files for later retrieval
• Checking script errors and exit codes

Investigate Python failures in a sandbox, an isolated execution environment, by capturing partial logs, testing functions separately, and checking output files one at a time. Use the findings to identify failing steps and rebuild a working script.

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• Investigate failures with missing or incomplete output
• Isolate a failing function in a large script
• Verify multiple generated files one at a time

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

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

Recover from failed code execution by simplifying code, switching to shell commands, or handing complex tasks to shell_agent. Explicit working directories and output checks help keep generated files in the expected location.

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• Recovering from sandbox execution failures
• Keeping generated files in the intended folder
• Checking output files after code execution

Continue Python tasks when the e2b sandbox, an isolated execution environment, is unavailable. Guides switching from execute_code_sandbox to run_shell, preparing dependencies, checking execution results, and saving intermediate data between calls.

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• Continue Python processing during an e2b service outage
• Run CSV summaries after sandbox connection failures
• Execute multiline Python scripts through run_shell

Run Python code through a saved script and shell when repeated sandbox failures point to environment problems. Captures execution output and checks exit codes and expected files to verify whether the task completed.

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• Continuing work after repeated sandbox execution failures
• Running Python scripts saved in the workspace
• Investigating failures through shell error output

Recover from failed file and directory operations by switching to run_shell, a shell command tool. Create nested folders, write files, and verify each result before continuing with tasks that depend on those files.

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• Retry file creation after an unknown error
• Create nested project folders explicitly
• Write files containing multiple lines

Run Python through a saved script when execute_code_sandbox or shell_agent repeatedly fails. The workflow writes code to a file, executes it through run_shell, and checks errors and expected outputs such as spreadsheets or documents.

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• Retrying Python tasks after execution tools fail
• Creating Excel files with a saved Python script
• Checking error output and generated files

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.

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• Reading text or JSON when read_file fails
• Inspecting Excel and CSV data through Python
• Extracting text from PDF and Word documents

Translate user needs into concise stories that explain who benefits, what they want, and why. Add Gherkin acceptance criteria—preconditions, an action, and an expected result—to support development discussions and testable definitions of success.

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• Turning requirements into user stories
• Defining testable acceptance criteria before development
• Preparing stories for backlog refinement and sprint planning

Turn product management notes or workshop material into a structured skill, or update an existing one. Follow a workflow covering duplicate checks, drafting, validation, catalog updates, and optional ZIP packaging for upload.

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• Turning workshop notes into a product management skill
• Updating a skill while preserving repository standards
• Checking structure and metadata before committing

Turn product management notes, frameworks, or rough ideas into a complete SKILL.md draft. Guided questions help choose the skill type, narrow its purpose, and organize instructions, examples, and common mistakes into the required structure.

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• Turning workshop notes into an agent skill
• Choosing the right structure for a new skill
• Formalizing a product management framework

Build, refactor, and troubleshoot ChatGPT apps with an MCP server, which exposes tools to the assistant, and an interactive widget. Use official documentation and examples to plan tools, scaffold code, configure security settings, and validate local operation.

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• Scaffolding a ChatGPT app with a server and widget
• Planning app tools and their inputs
• Refactoring an existing Apps SDK demo

Turn a selected screenshot, mockup, or generated image into a responsive website or web app. Recreate its layout and visual assets, implement core interactions, and compare the running result with the reference in a browser.

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• Build an interactive prototype from a mockup
• Recreate a screenshot as a responsive web page
• Implement a mobile screen from a selected reference

Guides development of apps for viewing USD, a 3D scene format. Selects browser streaming or local implementation references, covering rendering, camera controls, selection, interface layout, and validation while keeping users’ scene files unchanged.

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• Build a browser viewer for USD scenes
• Develop a local workstation viewer
• Add camera controls and object selection