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Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.
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Plan, train, evaluate, and deploy neural networks using Flow Nexus cloud infrastructure. Coordinate training across multiple computing environments, tune training settings, and manage model versions, predictions, and ongoing performance monitoring.
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Classify market conditions as bullish, bearish, ranging, or volatile to guide strategy selection and historical testing. Distinguish synthetic demonstrations from analysis of actual price data, with confidence levels and historical comparisons where available.
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Analyze Git changes before submitting or merging a pull request. Assess risk and complexity, classify changes, suggest reviewer roles, and identify files needing closer inspection using diff statistics and file-level risk analysis.
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在将用户投稿或API接收的文本交给模型之前进行检查，使用AIDefence识别企图劫持指令、窃取信息等威胁，并查看威胁分类及判定置信度。
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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