'execution' 검색 결과

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

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

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

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 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

Identify agent failures that happen before any work starts by checking iteration counts, tool use, artifacts, and logs. Classify these incidents as pre-execution failures and guide investigation toward environment setup, initialization, and resource limits.

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• Investigating agent sessions with zero completed iterations
• Classifying failures before the first tool call
• Routing incidents to infrastructure or platform teams

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

Analyze agent runs that fail before any work begins. By checking execution counts, tool calls, outputs, and conversation logs, this skill classifies startup failures and directs investigation toward system setup, resources, or input processing.

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• Investigating failed runs with zero execution steps
• Classifying failures with no tool calls or outputs
• Routing startup incidents to infrastructure teams

Assess server-side template injection, where user input is interpreted as template code. Identify template engines, evaluate information exposure and code execution risks, and organize evidence alongside detection and prevention measures.

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• Assessing template rendering endpoints
• Identifying the template engine in use
• Evaluating information exposure and code execution risks

Assess security weaknesses in CI/CD systems that automate software builds and delivery. Covering GitHub Actions, Jenkins, GitLab CI, and Azure DevOps, the skill examines unsafe inputs, excessive permissions, exposed credentials, and tampered build outputs, alongside detection and defensive controls.

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• Reviewing workflow inputs and execution triggers
• Assessing pipeline permissions and credential exposure
• Checking build artifact handoffs for tampering risks

Turn security findings into candidate code fixes with review results and summaries. Depending on the input, use independent source review or a pipeline with execution checks, then provide diffs for a person to review and apply.

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• Generating candidate fixes from triaged findings
• Preparing diffs for the highest-priority vulnerabilities
• Independently reviewing proposed security fixes

Probe AI agents through dialogue to check for unexpected command execution, file access outside intended paths, and prompted network requests. It selects tests based on available tools and records confirmed behavior with conversation evidence and mitigation suggestions.

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• Checking agents that can run commands or code
• Testing file access beyond intended directories
• Checking whether prompts trigger URL requests

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

Diagnose MSBuild failures from an existing binary build log. Inspect errors, settings, project references, and execution details to explain failures that console output alone does not clarify, including failures across multiple projects.

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• Investigating build errors with unclear console output
• Tracing cascading failures across multiple projects
• Checking MSBuild target execution order

Implement and test Go codemods, automated migration routines used by gh aw fix. The guide covers targeted configuration changes, preserving comments and document content, safe repeated execution, and checks for registration and execution order.

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• Add a migration for deprecated configuration
• Update an existing migration routine
• Test preservation of comments and formatting

Add an agent execution engine to gh-aw using a shared Markdown definition where possible. The skill guides installation, authentication, configuration, and testing, with Go infrastructure changes reserved for capabilities that existing settings cannot express.

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• Adding an agent execution engine to gh-aw
• Defining CLI installation, authentication, and MCP settings
• Extending shared behavior settings when existing options fall short

Analyze gh-aw execution traces, which record individual processing steps, to investigate slow or failing workflows. Using local JSONL files or telemetry backends, the skill checks timing, errors, missing attributes, and trace connections to support focused improvements.

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• Investigating slow workflow or agent runs
• Comparing errors across workflows and execution engines
• Finding missing trace attributes or broken connections

Investigate unsafe deserialization, where restoring objects from untrusted data can trigger code execution. The skill covers Java, PHP, Python, .NET, Ruby, and related patterns, with detection clues and callback-based checks for confirming execution.

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• Finding serialized data in cookies and request bodies
• Reviewing unsafe object restoration in source code
• Testing deserialization paths with language-specific tools

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.

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• Testing avatar and attachment uploads
• Checking Office document and ZIP imports
• Assessing image and video processing

Build a software quality assurance process with structured test cases, execution tracking, bug severity levels, and quality metrics. Includes templates for progress reports and team handoffs, plus prompts for agent-driven test execution.

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• Setting up a software QA process
• Standardizing test cases and bug reports
• Tracking test progress and release criteria

Compare competing plans from AI agents against the actual task and available evidence. Choose one, combine useful parts, or request revisions, then produce a single execution handoff with ordered steps, verification checks, and reasons for rejected alternatives.

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• Compare plans from Codex and Claude Code
• Combine complementary implementation proposals
• Check migration and validation gaps before implementation

Create and run BB workflows that coordinate multiple worker agents. Organize independent tasks into parallel stages, verify outputs, pass structured results between steps, and inspect execution progress using validated JavaScript workflow scripts.

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• Review multiple changes and verify each finding
• Process independent items through several stages
• Pass structured results between worker agents

Create, review, and troubleshoot agent skills: reusable instructions stored in SKILL.md and supporting files. This skill guides description writing, file organization, activation testing, execution checks, and diagnosis when a skill fails to load or follow its intended process.

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• Creating a new SKILL.md and supporting file structure
• Diagnosing why a skill is not invoked
• Improving instructions when execution fails

Improve existing code through up to two optimization rounds, measuring execution time and memory use against a baseline. Verify that results remain correct, save the optimized code, and document changes, measured gains, and trade-offs in a report.

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• Improving the performance of existing code
• Comparing execution time and memory use with a baseline
• Checking that optimized code produces correct results