Data analysis, machine learning, LLM apps, and prompt design (9K skills)
Write and edit system prompts, tool instructions, and agent definitions with clear structure and explicit rule strength. It guides tag use, placement of critical constraints, concise wording, and practical examples that show how tools should be used.
Review and shorten tool instructions for AI agents by testing overlap with parameter schemas, the definitions of accepted inputs. It also guides writing clear usage rules while preserving warnings backed by past failures.
Examine AI research directions and safety strategies through thinking models drawn from Ilya Sutskever’s public statements. The skill checks current facts when needed and presents a concise judgment with an everyday analogy.
Examine AI product reliability, learning methods, and model limitations through frameworks drawn from Andrej Karpathy’s public statements. Assess the gap between demonstrations and real use, uneven capabilities, and where people should retain control.
Create a reusable SKILL.md from research into a person’s thinking or a topic’s methods. Start with a name or a broad need, then extract mental models, decision rules, expression patterns, and limitations into a working skill.
Use the weknora CLI to manage knowledge bases and documents, search stored material, and ask questions grounded in it. This shared guide covers authentication, structured JSON results, error handling, and previews of changes.
Choose the right WeKnora command to answer questions from a knowledge base or retrieve source passages. Covers cited answers, custom agents, document searches, and continuing a conversation through the command-line tool.
Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.
Forecast sales, demand, or sensor readings with Google's pretrained TimesFM model, without custom training. Check hardware and dataset memory needs, include external factors such as prices or holidays, and obtain forecasts with uncertainty ranges.
Compose compact, structured prompts for coding, debugging, review, and research with Codex or GPT-5.4. This internal skill defines task scope, completion criteria, output format, verification steps, and evidence requirements using consistent XML tags.
Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.
Helps design ClickHouse tables, write analytical queries, and build data ingestion workflows. Covers batch inserts, precomputed summaries, and performance monitoring for teams working with large datasets and time-based activity analysis.
Build custom interactive charts, maps, and network diagrams with d3.js. The skill guides data preparation, styling, resizing, and interactions such as zooming and tooltips, giving detailed control over how data appears and responds to users.
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.
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.
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.
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.
Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.
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.
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.
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.
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.
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.
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.