'json output' 검색 결과

High-performance C JSON parsing, creation, and modification with yyjson. Use when working with yyjson documents, string copy-vs-borrow ownership, dangling c_str() bugs, yyjson_alc allocators, or writer output freeing.

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Detects MSBuild projects with conflicting OutputPath or IntermediateOutputPath. USE FOR: builds failing with 'Cannot create a file when that file already exists', 'The process cannot access the file because it is being used by another process', intermittent build failures that succeed on retry, or missing/overwritten outputs in multi-project or multi-targeting builds where bin/obj (or project.assets.json) collide. Common causes: shared OutputPath, missing AppendTargetFrameworkToOutputPath, extra global properties (e.g. PublishReadyToRun), or SetTargetFramework on a ProjectReference to a single-targeting project. DO NOT USE FOR: file access errors unrelated to MSBuild (OS-level locking), single-project single-TFM builds, non-MSBuild build systems.

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Detects MSBuild projects with conflicting OutputPath or IntermediateOutputPath. Only activate in MSBuild/.NET build context. USE FOR: builds failing with 'Cannot create a file when that file already exists', 'The process cannot access the file because it is being used by another process', intermittent build failures that succeed on retry, missing outputs in multi-project builds, multi-targeting builds where project.assets.json conflicts. Diagnoses when multiple projects or TFMs write to the same bin/obj directories due to shared OutputPath, missing AppendTargetFrameworkToOutputPath, or extra global properties like PublishReadyToRun creating redundant evaluations. DO NOT USE FOR: file access errors unrelated to MSBuild (OS-level locking), single-project single-TFM builds, non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, projects, evaluations, properties, double_writes); falls back to dotnet msbuild binlog replay + grep when the MCP is unavailable.

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Generate a read-only security report on threats involving MCP connections and operations. It groups findings by threat, reports the highest severity, and supports table or JSON output with a configurable severity threshold for failure.

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• Prepare security findings before a release
• Share a categorized threat report with a security team
• Check changes in MCP-related threats during periodic audits

Review invoices, expense receipts, and service contracts against accounts-payable policy before human approval. Recalculate invoice totals, flag missing documentation and risky contract terms, and produce a JSON report with findings and a review decision.

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• Recalculate invoice totals and report exact differences
• Check receipts and documented purchase approvals
• Flag changed bank details or payment instructions

Evaluate an Agent Skill bundle before release by checking structure, activation conditions, output quality, scripts, safety, file integrity, and host compatibility. Receive a detailed JSON report that distinguishes local evidence checks from production readiness backed by external attestation.

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• Reviewing a skill bundle before distribution
• Evaluating activation accuracy and output improvements
• Checking scripts, safety, and distributed files

Use Hermes Agent to create or refine a Python command-line interface for existing software. It guides source analysis, command design, interactive operation, JSON output, backend testing, documentation, and verification that the generated package installs locally.

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• Build command-line access to a GUI application
• Extend an existing interface with missing software features
• Test individual commands and complete backend workflows

Use Reasonix to build or improve a Python command-line interface for existing software. The workflow covers source analysis, interactive commands, JSON output, tests, documentation, and installation checks using the software’s own backend where possible.

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• Build command-line access to a GUI application
• Add missing features to an existing command-line interface
• Test commands against the actual software backend

Control a running iTerm2 workspace from the command line. Inspect processes and directories across panes, send commands, read terminal history, arrange windows and tabs, and manage preferences or tmux integration.

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• Check processes and directories across panes
• Send commands and read output history
• Create split panes and restore layouts

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

Prepare OBS Studio scene collections through commands. Add scenes and sources, adjust layout and audio, configure transitions and output settings, and save the project as JSON with undo and redo support.

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• Create and duplicate scenes for a broadcast
• Adjust source position, size, and cropping
• Set audio volume, mute, and monitoring

Delegate a clearly scoped coding task to Qoder in a specified project without opening its interactive interface. Read the structured JSON response and check its error flag before accepting the result.

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• Ask Qoder to review a specific file without editing
• Delegate a clearly scoped coding task to Qoder
• Check errors in Qoder's structured response

Delegate a clearly scoped repository analysis or implementation task to Pi without an interactive session. The skill limits the working directory and available tools, then checks structured output for errors before using the result.

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• Delegate a read-only code diagnosis to Pi
• Assign Pi an explicitly authorized implementation task
• Check Pi's structured output for model or tool failures

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

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• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Analyze Chrome, Edge, and other Chromium browser profiles with Hindsight to reconstruct web activity. Combine browsing history, downloads, cookies, and other stored records into a chronological timeline, with XLSX, JSON, or SQLite output for investigation.

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• Reconstructing browsing activity during incident response
• Checking download times and source URLs
• Comparing cookies and history in insider threat investigations

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Review the next upcoming meeting, including attendees, its description, agenda, and linked documents. Choose a calendar and an output format to prepare for the meeting without changing stored data.

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• Check attendees and details before the next meeting
• Prepare using a specific work calendar
• Retrieve meeting details as JSON or a table

Process AI model responses through a specified Google Model Armor template before sending them to users. Supply response text directly, pipe output from another command, or provide a JSON request for outbound safety processing.

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• Check model responses before showing them to users
• Pipe model command output into safety processing
• Process response text with a specified template

Build a workflow for importing large collections of audio, email, documents, or chats into knowledge pages. Test varied samples first, track progress in a persistent JSON file, and verify outputs before marking work complete.

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• Importing a large email export into knowledge pages
• Converting document collections into linked pages
• Resuming imports that span multiple sessions

Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.

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• Audit existing articles for evidence and credibility gaps
• Revise article structure and references for AI answers
• Record citations observed across AI services

Build n8n workflows that download, transform, and send files without losing their contents. Learn how file data differs from JSON, how to preserve attachments, and how to use storage keys or URLs with AI tools and chat messages.

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• Fixing attachments lost during data transformation
• Passing uploaded files to AI agent tools
• Displaying generated images in chat

Summarize ScenarioRunner results to see which scenarios passed and which checks failed. Compare runs before and after changes, or analyze recorded driving to produce measurements and plots of vehicle distance and deviation from the lane center.

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• Checking scenario pass and fail results
• Comparing results before and after controller changes
• Plotting vehicle distances from a recorded run

Render landscape videos with KrillinAI CLI, combining original footage with bilingual subtitles or prepared dubbed footage with target-language subtitles. Optionally replace audio, adjust subtitle styling, and verify the output video and subtitle placement.

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• Render landscape videos with bilingual subtitles
• Add target-language subtitles to prepared dubbed footage
• Replace audio or customize subtitle styling

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules