'classification' 검색 결과

Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.

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• Train an image classification model
• Forecast time series with LSTM
• Deploy a sentiment analysis template

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

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• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Build .NET workflows that extract text, tables, and fields from invoices, receipts, and other documents using Azure AI Document Intelligence. Covers prebuilt models, custom model creation, document classification, authentication, and result handling.

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• Extract vendors, line items, and totals from invoices
• Read merchants, dates, and totals from receipts
• Extract text and table cells from documents

Build Java applications that extract text, tables, and fields from documents using Azure AI Document Intelligence. Covers ready-made models for invoices and receipts, custom model creation, document classification, authentication, and error handling.

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• Extract text and tables from PDFs
• Read invoice and receipt fields
• Build models for custom document formats

Integrate harmful-content checks into Python applications using Azure AI Content Safety. Analyze text and images by category and severity, and manage custom text blocklists to help review user submissions or AI-generated content before display.

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• Checking user-submitted text for harmful content
• Analyzing harmful-content categories in uploaded images
• Reviewing AI-generated text before displaying it

Helps implement document processing in TypeScript with Azure, extracting text, tables, invoice fields, and receipt details. Covers ready-made models, custom model training, document classification, authentication, and waiting for analysis results.

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• Extracting text and tables from PDFs
• Reading invoice and receipt fields
• Training models for specialized documents

Configure permission checks for unattended Antigravity CLI runs. The setup combines blocking rules, automatic approval for scoped operations, AI classification for ambiguous calls, and single-use approval tokens to control tool access within a verified workspace.

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• Setting up permission checks for unattended Antigravity CLI runs
• Defining rules for shell commands, file changes, and network access
• Configuring a local model to classify ambiguous tool calls

Draft employment contracts, job offer letters, employee handbooks, and HR policies. The skill helps organize consistent employment documents around worker classifications and local requirements, with templates intended for customization and review by qualified legal counsel.

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• Draft employment contracts
• Prepare job offer letters
• Write employee handbooks and HR policies

Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.

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• Restrict which tools an agent can call
• Check suspicious inputs before execution
• Require human approval for sensitive operations

Classify skills CLI pull requests, or proposed code changes, as documentation, bug fixes, or enhancements using their descriptions and patches. Add one label when the evidence is clear, while preserving existing classification labels.

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• Label changes that only update documentation or examples
• Classify fixes to existing CLI behavior
• Identify new commands or expanded agent support

Investigate suspected data theft from AWS S3 by comparing access logs, GuardDuty findings, and Macie classifications. Identify unusual bulk downloads and transfers across accounts, assess sensitive data exposure, and build automated detection rules.

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• Investigating unusual bulk downloads from S3
• Checking transfers to external AWS accounts
• Assessing exposure of sensitive data

Analyze ModSecurity, AWS WAF, and Cloudflare logs for SQL injection, attempts to manipulate database queries through web requests. Groups related requests, tracks source IPs, classifies attack methods, and produces incident reports.

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• Investigating suspected SQL injection attempts
• Tracking repeated attacks from the same source
• Connecting requests across an attack campaign

Analyze DNS logs for signs of hidden command traffic and data theft. Use character randomness, query timing, TXT record inspection, and machine learning to identify suspicious domains and support security monitoring rules.

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• Investigating suspected DNS tunneling
• Classifying algorithmically generated domains
• Detecting periodic DNS beacon traffic

Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

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• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

Set up an email reporting button and an automated security response workflow. Classify reported messages, extract attack indicators, remove confirmed phishing emails, and send feedback to the people who reported them.

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• Deploying a phishing reporting button in Outlook
• Automating triage of a dedicated reporting mailbox
• Retracting confirmed phishing emails across inboxes

Observe malware in an isolated Cuckoo Sandbox environment and review its processes, network traffic, and system changes. Use the findings to trace infection stages, identify persistence mechanisms, and extract indicators of compromise for behavioral reports.

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• Investigating suspicious files after static analysis
• Tracing multi-stage infections and persistence mechanisms
• Extracting indicators of compromise from observed behavior

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

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.

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• Checking prompts before sending them to an LLM
• Filtering responses before displaying them
• Building a moderation API

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Consolidate inconsistent gbrain page types into the standard gbrain-base-v2 classification. Preview proposed changes, convert redirect pages into aliases and relationship pages into links, then verify the migration with original type information retained for rollback.

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• Consolidate inconsistent page types
• Preview a migration to gbrain-base-v2
• Convert redirect pages into aliases

Organize the outputs of a substantial work session into linked knowledge pages with sources. A seven-phase review checks classification rules, identifies reusable workflows, audits overlapping skills, and verifies that saved pages and skills can be found.

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• Saving the findings from a completed research session
• Organizing outputs from a development session
• Finding workflows worth turning into reusable skills

Review open Mastra GitHub issues and trace reported defects to source code. Apply the @mastra/core label to confirmed core bugs, report uncertain ownership, and summarize whether fixes are active, stale, or unclaimed.

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• Checking whether reported bugs belong to @mastra/core
• Labeling confirmed core bugs with supporting evidence
• Previewing issue classifications without changing GitHub

Triage a GitHub issue by reviewing its discussion, related issues, and fixing pull requests. Publish a concise comment with classification, severity, evidence, and the next action, then update a small set of triage labels.

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• Classifying newly opened GitHub issues
• Requesting missing reproduction details
• Identifying duplicates and existing fixes