'batch' 검색 결과

Helps build Python workflows for translating Word, PDF, Excel, PowerPoint, and other documents while preserving formatting. Covers Azure batch translation, multiple target languages, terminology glossaries, and checks for job progress and document errors.

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• Translating batches of documents while preserving formatting
• Translating documents into multiple languages
• Applying a glossary for specialized terminology

Use Azure's Python transcription client to turn audio files into text and review timestamped results. Covers authentication, installed SDK options, and error handling, while clarifying when streaming or asynchronous batch work needs a different Speech API.

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• Transcribe a local recording with consent
• Review timestamped transcription output
• Check authentication and installed SDK options

Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.

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• Schedule document ingestion and RAG index refreshes
• Design batch workflows for LLM requests
• Connect model training with quality evaluation

Implement Python error handling with early input checks, appropriate exception types, and clear messages. Handle individual failures in batch jobs, preserve error context for debugging, and use Pydantic models to validate structured input.

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• Validating user input and API parameters
• Designing exception types and clear error messages
• Continuing batch jobs after individual failures

Split a large goal into clearly bounded tasks that can run in parallel. Coordinate existing agent and worktree tools, then combine and verify the results. The skill starts only when explicitly requested.

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• Splitting a large goal into parallel tasks
• Coordinating work across multiple agents
• Running tasks in separate worktrees

Resize images, convert formats, create thumbnails, and inspect dimensions and metadata with ImageMagick. Process individual files or batches, selecting images by size or file type and preserving their proportions when resizing.

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• Create thumbnails from a folder of images
• Resize wallpapers for different screen sizes
• Convert image file formats

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

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• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Audit a public website for search visibility and AI readability, then prepare prioritized fixes, configuration files, and revised copy. For connected stores, review product listings and apply approved batches of updates to titles, descriptions, and SEO fields.

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• Audit a website for search and AI readability
• Prepare crawler settings and machine-readable business data
• Rewrite page titles, descriptions, and content

Turn text into audio files for narration, product demos, phone prompts, or accessibility reads using the OpenAI Audio API. Choose built-in voices and delivery directions, then generate a single clip or a batch of files.

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• Create narration from an explainer script
• Generate a voiceover for a product demo
• Produce a batch of phone menu prompts

Generate images from text or reference images using multiple image services. Choose aspect ratios, sizes, and quality settings, or generate a collection of images in batches from saved prompt files.

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• Create illustrations from text descriptions
• Change scenes using reference images
• Generate images in specific aspect ratios

Review NemoClaw issues and pull requests, propose issue types, project priorities, statuses, and labels, then apply only approved changes. Supports individual items and batch triage, with explanations and a report of each update.

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• Classify a newly opened issue
• Review priorities and statuses for issues and pull requests
• Triage unlabeled items in a batch

Collect weekly snack requests from Slack, combine duplicate items with standing staples, and prepare a vendor cart within budget. Share the draft for the office manager's written approval before placing the order.

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• Batching office snack requests each week
• Combining duplicate requests and standing staples
• Adjusting a draft order to fit the weekly budget

Run batches of machine learning experiments on remote GPUs over SSH, varying settings and random seeds. The skill schedules jobs, tracks progress, retries GPU memory failures, and cleans up leftover sessions before starting subsequent batches.

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• Run experiments across multiple random seeds
• Test combinations of model settings
• Chain teacher training and student distillation

Coordinate large code migrations in reviewable batches. The agent changes local files, while the Composio CLI connects tracking issues, pull requests, and automated checks so each batch can be tested and reviewed before proceeding.

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• Upgrading frameworks across many files
• Renaming an API throughout a monorepo
• Migrating configuration files or test tools

Create presentations from ideas or outlines through the Banana Slides API, then export them as PPTX, PDF, or images. Also supports batch generation, redesigning existing presentations, and editing slide images with written instructions.

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• Generate a presentation from an idea or outline
• Export slides as PPTX, PDF, or images
• Generate multiple presentations in a batch

Turn supplied text, files, or approved URLs into linked Obsidian notes with source records and evidence tracking. Review proposed changes before applying them, preserve original materials, and record contradictions or gaps in support.

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• File research materials with their sources
• Connect new sources to existing notes
• Track conflicting claims and missing evidence

Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.

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• Deploying an OpenAI-compatible language model API
• Generating responses for a large set of prompts
• Fitting a large model into limited GPU memory

Use Meta's Prompt Guard to detect malicious instructions in user prompts and external data. This skill guides input filtering, batch checks for retrieved documents, detection threshold adjustments, and splitting long text for inspection.

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• Detecting attempts to override an LLM's instructions
• Checking API responses for embedded malicious instructions
• Batch screening documents retrieved for RAG

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

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• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Add documents, interview notes, or observations to an open investigation. Process batches against documented selection criteria, surface significant items, and log everything reviewed so the team can verify coverage and update its source checklist.

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• Add interview notes to an open investigation
• Process a newly received batch of documents
• Add an email export to an investigation

Extract the same fields from a batch of contracts or other documents into a comparison spreadsheet. Each answer includes a source quote and location, while missing, ambiguous, and review-required information is clearly distinguished for human verification.

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• Compare contract clauses during M&A due diligence
• Audit vendor contracts using a shared set of fields
• Review dates and notice periods across a lease portfolio

Diagnose file access problems by checking directories, permissions, file headers, and line counts in parallel batches. Also reviews TypeScript imports, fixes duplicates and type-only imports, and checks module exports before retrying the failed operation.

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• Investigate missing files, incorrect paths, and permission errors
• Review multiple source files in parallel batches
• Check CSS and configuration file headers and line counts

Plan or run bulk RNA-seq analysis to find genes whose expression differs between conditions. Check sample replication, study design, batch effects, and data quality, then produce comparison tables, diagnostic plots, and ranked gene lists.

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• Comparing gene expression between experimental conditions
• Planning analyses with donor pairing or batch variables
• Checking sample quality with PCA and distance plots

Turn descriptions or code analysis into Mermaid diagrams for workflows, system architecture, and database relationships. Check diagram syntax and export SVG, PNG, or text-based ASCII output, with themes and batch rendering options.

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• Visualizing system architecture
• Documenting API request sequences
• Drawing database relationships