'execution' 검색 결과

Find JavaScript that executes strings as code, including eval(), new Function(), and string-based timers. Review how it runs in the browser and suggest alternatives for parsing data, choosing operations, and scheduling functions to reduce code injection risks.

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• Finding eval() and other string execution
• Replacing eval() used to parse JSON
• Changing string-based timers to function references

Connect syncable entity components within Twenty server internals and expose them through GraphQL, an API query interface. It guides NestJS module registration, business services, response data conversion, and consistent handling of validation and execution errors.

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• Register builders, validators, and action handlers in NestJS modules
• Connect entity creation to validation and migration execution
• Expose entities through GraphQL with response data conversion

Set up and inspect an Ethereum node through a command-line interface. Install execution and consensus clients, configure validator metadata, expose RPC access for external requests, and retrieve structured health reports for agent workflows.

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• Set up a new Ethereum node host
• Install chosen execution and consensus clients
• Configure RPC access through Nginx or Caddy

Manage n8n automation workflows from the command line. Create, update, and activate workflows, inspect or retry executions, and manage credentials, variables, and tags through n8n's public API with structured JSON output.

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• Create or update an n8n workflow
• Activate or deactivate an automation workflow
• Inspect execution history and retry a run

Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.

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• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

Learn how to check Claude Code tool calls against a policy and create digitally signed execution records. This walkthrough covers setup, offline verification, tamper demonstrations, and CI/CD integration before adopting the protect-mcp runtime plugin.

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• Evaluate signed audit records before adopting runtime hooks
• Demonstrate offline verification and tamper detection
• Verify agent execution records in CI/CD

Improve Bazel builds in large repositories containing multiple projects. Get guidance on build configuration, shared remote caches, remote execution, dependency management, custom build rules, and diagnosing build problems.

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• Set up Bazel for a monorepo
• Configure remote caching and execution
• Review slow build configurations

Assess serverless functions, primarily AWS Lambda, for unsafe execution of external input and unauthorized changes to code or added libraries. Combine source inspection, activity logs, and permission reviews to identify injection risks and recommend detection and prevention measures.

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• Reviewing Lambda event handlers for injection vulnerabilities
• Investigating unauthorized function or layer changes
• Checking privilege escalation through Lambda execution roles

Analyze Windows Amcache records to investigate applications, deleted executable traces, and drivers. Extract file details, compare SHA-1 hashes with malware databases, and combine records into a timeline to support incident investigations.

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• Investigating traces of deleted executables
• Checking software installed during an incident
• Comparing file hashes with malware databases

Guides background processing in Node.js and TypeScript with BullMQ and Redis. Covers job queues, which hold tasks for later execution, along with scheduling, priorities, rate limits, worker concurrency, and dependencies between processing steps.

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• Design a queue for background tasks
• Schedule jobs for later or repeated execution
• Organize dependent steps into a job flow

Guides experiments that inspect and change internal model values using nnsight. Covers local PyTorch models and remote experiments through NDIF, including replacing intermediate values to test how particular layers affect a model’s predictions.

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• Inspecting layer outputs and attention patterns
• Replacing internal values to compare predictions
• Running large-model experiments through NDIF

Build collaborating AI teams with CrewAI by defining roles, tasks, and shared context. Configure ordered execution or manager-led delegation, add memory and tools, and create workflows that branch based on events or results.

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• Build a research-to-writing agent workflow
• Coordinate specialists through a manager agent
• Create workflows with conditional branches

Create, configure, and troubleshoot Crush hooks, commands that run before tool execution. Use them to block or approve calls, rewrite tool input, or add context for the agent, with guidance on configuration and troubleshooting.

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• Blocking destructive shell commands
• Automatically approving read-only tools
• Adding editing reminders for the agent

Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.

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• Sharing common logic across workflows
• Splitting a large workflow into reusable parts
• Exposing shared processing as an AI agent tool

Give subagents or short sessions a compact set of working rules for checking evidence, changing unsuccessful approaches, and verifying results. It also defines agent roles and uses stern corporate-style commentary to push execution forward.

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• Giving subagents a shared set of execution rules
• Adding evidence checks to short working sessions
• Changing approach after repeated failures

Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.

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• Add sentiment analysis to a JavaScript app
• Build image classification in a browser
• Implement audio transcription

Investigate errors, crashes, and unexpected behavior by reproducing failures, tracing execution, and comparing logs. Tests one explanation at a time, then provides a verified fix, supporting evidence, and tests or safeguards to prevent recurrence.

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• Investigate error messages and execution traces
• Find the cause of an application crash
• Compare logs to locate a failure

Turn an idea into a goal package for /goal. Collect decisions through Plannotator, review testable requirements, explore the codebase, and produce an approved execution plan with verification steps and a clear completion condition.

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• Turning a rough feature idea into testable requirements
• Agreeing on scope and success criteria before implementation
• Creating an execution plan grounded in the codebase

Run Python through run_shell when code execution tools repeatedly fail. This workflow embeds a multiline script directly in a shell command, supporting tasks such as CSV conversion and PDF generation while checking execution output.

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• Continuing Python work after repeated tool errors
• Converting CSV data to JSON
• Generating PDFs with reportlab

Recover Python tasks when execute_code_sandbox fails with errors or timeouts. Install required packages, run the code through run_shell, and inspect messages and generated files to check the result.

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• Resuming Python tasks after sandbox failures
• Installing missing packages before execution
• Creating a Word document through shell execution

Recover from failed automated file creation with an explicit shell workflow. The skill guides agents through checking folders, writing and running scripts, and inspecting output files to confirm that deliverables were actually created.

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• Recovering from repeated automated execution failures
• Checking where output files were saved
• Verifying generated PDF and Word files

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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• Retrying PDF generation after sandbox execution fails
• Continuing DOCX or XLSX file operations after errors
• Checking Python packages needed to run a script

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

Diagnose Python script failures by capturing full error traces, which show where execution failed, alongside exit codes and working directory details. The skill also covers file existence checks and clearer paths for troubleshooting.

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• Investigate Python failures with unclear error messages
• Find why a script cannot locate its input file
• Add diagnostic output to file processing scripts