Daniel Avila

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

Provides prompt templates for coding, writing, analysis, and creative work. Helps define the AI's role, specify the desired output, add examples and constraints, and refine requests using a practical checklist.

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• Draft a prompt for code review
• Create requests for clear technical documentation
• Prepare prompts for feature and naming ideas

Build similarity search with FAISS to find nearby matches in large collections of numeric vectors. The skill guides index selection, memory reduction, GPU use, and saving indexes while balancing search speed and accuracy.

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• Find similar items in large vector datasets
• Choose an index for search speed and accuracy
• Reduce memory use for vector search

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Approach engineering problems by checking assumptions, defining performance constraints, and choosing suitable tools before implementation. The skill guides algorithm analysis, complete code, performance optimization, and correctness checks through tests, assertions, or formal verification, meaning mathematical checks of correctness.

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• Checking algorithm complexity and design assumptions
• Choosing languages for numerical or distributed systems
• Implementing concurrent code with explicit error handling

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Execute an implementation plan in one session by assigning a fresh subagent, a separate task-focused agent, to each task. Check specification compliance first, then code quality, and repeat fixes and reviews before proceeding.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Execute an existing plan with mostly independent tasks
• Check specification compliance before reviewing code quality
• Repeat fixes and reviews for each implementation task

Plan voice AI systems by comparing direct speech processing with separate transcription, language generation, and speech synthesis stages. Covers response delays, detecting when people speak, interruptions, and noise handling to guide conversational interface design.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare architectures for a voice AI system
• Review delays across conversation processing stages
• Design handling for interruptions during speech

Plan A/B tests that compare page or product changes, with clear hypotheses, sample sizes, success metrics, and traffic allocation. Get a structured test plan and guidance for interpreting results and deciding what to try next.

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• Comparing signup button copy
• Planning a pricing page experiment
• Estimating sample size and test duration

Turn large product requirements into user stories with acceptance criteria, effort estimates, and priorities. The toolkit supports backlog management, sprint planning based on team capacity, and tracking how much work the team completes over time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Breaking large requirements into user stories
• Preparing acceptance criteria and effort estimates
• Prioritizing the development backlog

Guide the design of AI product features with output validation, responses displayed as they arrive, prompt versioning, and testing. Address API costs, failures, and unsafe input when preparing language model integrations for production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing AI features before production release
• Designing validation for AI-generated output
• Managing prompt versions and regression tests

Plan, implement, and audit website or app analytics with GA4 and Google Tag Manager. Define meaningful events, standardize campaign tags, and validate data collection so marketing and product decisions use consistent measurements.

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• Plan tracking for signups and purchases
• Review existing GA4 and GTM tracking
• Standardize UTM campaign naming

Improve mobile app listings on Apple App Store and Google Play Store through keyword research, clearer descriptions, visual asset guidance, and review analysis. Create competitor reports, A/B test plans, and launch checklists to guide store page improvements.

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• Revising app titles, descriptions, and keywords
• Comparing competing apps' store listings
• Finding complaints and feature requests in reviews

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Style materials with Anthropic's brand colors and typography. Apply distinct heading and body fonts, choose text colors for the background, and use coordinated accent colors for shapes, with fallback fonts when Poppins or Lora are unavailable.

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• Styling slides with Anthropic's brand colors
• Applying consistent heading and body fonts
• Coordinating shape and accent colors

Use Perplexity to find web resources, recent information, or clear explanations drawn from the web. The skill chooses between search results and conversational answers, while directing specialized documentation and workspace questions to other tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding recent information on a topic
• Discovering tutorials and blog posts
• Getting web-based explanations of concepts

Guides language model training with GRPO in TRL, comparing several responses and rewarding better results. Covers dataset preparation, scoring rules for correctness and format, training configuration, monitoring, and saving models for later use.

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• Training models to follow JSON or XML formats
• Designing rewards for math and coding tasks
• Adjusting training to available GPU memory

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train mathematical reasoning models with GRPO
• Experiment with custom reward and loss functions
• Scale training across multiple GPUs or nodes

Set up reinforcement learning, which trains models using rewards, with verl. Follow workflows for preparing data, configuring GRPO or PPO training, scaling across GPUs, and addressing memory errors or unstable training.

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• Training a math reasoning model with GRPO
• Configuring PPO training with a critic model
• Scaling large model training across multiple nodes

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Guides Python agent development with PydanticAI, covering tool calls and structured outputs checked against defined data types. Explains service injection, provider switching, streaming responses, and tests that run without calling a real language model.

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• Building agents that return validated structured data
• Connecting customer support agents to order data
• Testing agent logic without real model calls

Design RAG systems that retrieve relevant documents to support LLM answers. The skill guides document splitting, meaning-based and keyword search, result reranking, and separate retrieval evaluation to improve the context passed to the model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing document retrieval for LLM answers
• Reviewing document splitting strategies
• Combining keyword and meaning-based search

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Guides large language model reinforcement learning with miles, covering environment setup, low-precision training, consistency between training and inference, and speculative decoding, where a smaller model proposes tokens for a larger model to verify.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure reinforcement learning for large MoE models
• Investigate instability in FP8 training
• Set up speculative decoding for faster rollout generation