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Found 93 Skills
Chain multiple AI steps into one reliable pipeline. Use when your AI task is too complex for one prompt, you need to break AI logic into stages, combine classification then generation, do multi-step reasoning, build a compound AI system, orchestrate multiple models, or wire AI components together. Powered by DSPy multi-module pipelines.
Auto-moderate what users post on your platform. Use when you need content moderation, flag harmful comments, detect spam, filter hate speech, catch NSFW content, block harassment, moderate user-generated content, review community posts, filter marketplace listings, or route bad content to human reviewers. Covers DSPy classification with severity scoring, confidence-based routing, and Assert-based policy enforcement.
Auto-sort, categorize, or label content using AI. Use when sorting tickets into categories, auto-tagging content, labeling emails, detecting sentiment, routing messages to the right team, triaging support requests, building a spam filter, intent detection, topic classification, or any task where text goes in and a category comes out.
Guide incident response from detection to post-mortem using SRE principles, severity classification, on-call management, blameless culture, and communication protocols. Use when setting up incident processes, designing escalation policies, or conducting post-mortems.
Write Domain-Driven Design architecture models using DomainLang (.dlang files). Covers domains, bounded contexts, context maps, teams, classifications, terminology, relationships, namespaces, and imports. Use when creating DDD models, mapping bounded context relationships, documenting ubiquitous language, or generating .dlang files for strategic design.
Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)
Data classification framework including sensitivity levels, handling requirements, labeling, and data lifecycle management
Detect language of text with confidence scores, support for 50+ languages, and batch text classification.
Use when asked to compare multiple ML models, perform cross-validation, evaluate metrics, or select the best model for a classification/regression task.
Use when "HuggingFace Transformers", "pre-trained models", "pipeline API", or asking about "text generation", "text classification", "question answering", "NER", "fine-tuning transformers", "AutoModel", "Trainer API"
Image processing, object detection, segmentation, and vision models. Use for image classification, object detection, or visual analysis tasks.
Provides brand typography selection and hierarchy development frameworks including the Brand-First Typography Selection Process, Modular Scale System, Font Classification Matrix, Serif vs. Sans-Serif Decision Framework, Typeface Evaluation Criteria, Font Pairing Principles, WCAG accessibility requirements, and typography design tokens. Auto-activates during brand typography development, font selection, type hierarchy creation, and typography system work. Use when discussing brand typography, font selection, font pairing, type hierarchy, modular scale, typography accessibility, WCAG typography, or typography guidelines.