데이터·AI 스킬

Track when assets begin moving together, assess the resulting loss of diversification, and examine which assets moved first during a crisis. Rank changes in asset relationships and report uncertainty explicitly for risk monitoring and retrospective analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitoring when assets start moving as one group
• Reporting reduced portfolio diversification
• Examining which assets moved first during a crisis

Analyze bid–ask spreads, order books, and buying and selling imbalances to assess liquidity, or how easily an asset trades. Estimate large-order price impact and transaction costs, with additional guidance on Chinese A-share auction and block trades.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate transaction costs for large orders
• Assess liquidity using order book depth and spreads
• Study trading imbalances and signs of liquidity stress

Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating backtest execution errors
• Finding why a strategy produces no trades
• Checking delayed trades or low capital usage

Rewrite lengthy prompts, tool descriptions, and agent instructions into compact text while preserving requirements, exceptions, and exact values. The process records removed content and compares token counts, the units models use to measure text length.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Shortening system prompts
• Compressing tool documentation and skill bodies
• Reducing text length for model input

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting system prompts for agents
• Clarifying required and optional instructions
• Writing tool explanations with runnable examples

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit tool instructions for repeated parameter explanations
• Test schema overlap before removing prompt lines
• Write tool selection rules and practical examples

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing AI research directions
• Examining model scaling and data limits
• Discussing AI safety strategies

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing AI product reliability beyond a demo
• Planning hands-on study of neural networks
• Examining LLM strengths and failure patterns

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a thinking adviser based on a named person
• Finding a suitable perspective for an unclear need
• Building a skill from personal writing and decision notes

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up authentication for a WeKnora server
• Managing knowledge bases and uploaded documents
• Searching stored material and asking questions about it

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Ask questions with citations from a knowledge base
• Retrieve source passages for further analysis
• Find documents by title or filename

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Forecasting future sales or demand from CSV data
• Including prices and holidays in sales forecasts
• Forecasting multiple stores' time series in batches

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Define implementation scope and completion criteria
• Include verification steps in debugging requests
• Specify evidence requirements for code reviews

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing tables for large analytical datasets
• Improving slow aggregation queries
• Analyzing user retention and conversion steps

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating datasets and tables
• Analyzing data with SQL queries
• Ingesting data and managing processing jobs

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating charts beyond standard library options
• Exploring relationships in a network diagram
• Adding zoom and tooltips to visualizations

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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

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

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refine prompts that produce inconsistent answers
• Create reusable templates with variables and conditions
• Select relevant input-output examples for a task

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Making AI summaries more consistent
• Structuring information extracted from support tickets
• Creating reusable code review prompts

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