@davila7
Building AI dev tools with LLMs
350 skills4 repositories 32.9K
Plan and configure large language model training with OpenRLHF to better match human preferences. Covers training method selection, distributed GPU setup, inference acceleration with vLLM, and troubleshooting memory or training stability issues.
Design and improve RAG systems, which retrieve documents to support language model answers. Explore meaningful document splitting, vector storage, combined keyword and semantic search, and relevance ranking while checking common causes of poor retrieval.
Analyze Google Analytics traffic, visitor engagement, and conversions, such as purchases or goal completions. Compare periods, identify strong and weak pages or traffic sources, and receive prioritized recommendations with implementation guidance.
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.
Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.
Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.
Turn a plain-language app request into a technology choice, project structure, and development plan. The skill coordinates specialist agents across interface, server, and database work, with templates for web, mobile, desktop, and other applications.
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.
Train language models with SimPO using pairs of preferred and rejected answers. Configure preference training without a separate reference model, adapt settings for base or instruction models, and address unstable training, memory limits, or lost capabilities.
Plan caching for repeated LLM requests by choosing what to reuse: shared prompt beginnings, complete responses, or documents loaded in advance. Covers Anthropic prompt caching, cache invalidation, and problems caused by changing inputs or outdated answers.
Design and refine prompts for language model applications. Choose useful examples, create reusable templates and system instructions, and compare prompt variations using accuracy, consistency, response time, and token usage to guide improvements.
Build voice applications and phone agents with guidance on OpenAI Realtime API, Vapi, Deepgram, ElevenLabs, and LiveKit. Choose services for speech recognition, voice generation, and live communication, while addressing response delays and interruptions.
Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.
Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.
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.
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.
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.
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.
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.
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.
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.
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.
Improve AI instructions with examples, clear output formats, and comparison tests. Use reusable templates and system prompts, which set ongoing behavior, to address inconsistent answers and investigate unexpected agent behavior.
Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.
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