데이터·AI 스킬

Prepare spreadsheet data for analysis by finding extra spaces, inconsistent capitalization, numbers stored as text, mixed date formats, and duplicates. Review proposed fixes, apply traceable formula-based changes, and compare results before and after cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Cleaning spreadsheet data before analysis
• Standardizing dates and text capitalization
• Finding duplicate rows and mixed data types

Use OpenViking through OpenClaw to manage long-term memories, search archived conversations, and find imported resources or Agent Skills. Inspect recall traces to understand search results and recover full tool outputs stored outside the conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recall past preferences and decisions
• Find exact commands in archived conversations
• Import and search documents or Agent Skills

Find relevant past execution experience in OpenViking before starting a task. Check when each experience applies and when it does not, then load suitable guidance while giving current facts, policies, and tool results priority.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find past experience relevant to a new task
• Exclude guidance that does not fit current conditions
• Recheck past guidance against current tool results

Turn documents, notes, research, or code into a knowledge graph that maps identifiable things and their relationships. Create or update entity files and typed connections, with source evidence attached for inspection and later visualization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Map people and organizations across documents
• Organize research with traceable evidence
• Map relationships between software modules

Set up the OpenViking long-term memory plugin for OpenClaw through conversation. The skill handles installation, configuration, and verification so chat facts can be saved and relevant memories recalled across sessions, using an existing OpenViking server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add long-term memory to OpenClaw
• Recall saved facts across chat sessions
• Separate memories by assistant or sender

Manually sync OpenClaw chat transcripts to OpenViking and search stored memories using explicit commands. Receive a sync summary or relevant memory entries while leaving the OpenClaw contextEngine slot available for its existing use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Manually sync recorded chat transcripts
• Find stored memories about a specific topic
• Check the summary of a manual sync

Turn local Q&A session files into an English weekly report with topic rankings, unresolved issues, activity metrics, and comparisons with the previous week. Include supporting message excerpts and structured data for later document and dashboard rendering.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a weekly community Q&A report
• Compare topics and activity with the previous week
• Review unresolved high-priority issues

Build systems that use Pinecone to find documents for AI answers or retain agent memories across sessions. Guides index setup, data loading, meaning-based and keyword search, memory separation, and end-to-end checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI assistant that answers from documents
• Keep agent facts and decisions across sessions
• Combine meaning-based search with exact terminology

Build and run evaluations for AI applications using Phoenix. Start with observed failures, create checks in code or use a language model as a judge, run experiments, and validate automated judgments against human labels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning observed failures into evaluation criteria
• Building code-based or language-model evaluators
• Evaluating retrieval and answer faithfulness in RAG

Review DAX formulas, the expressions used for calculations in Power BI, and propose clearer, more maintainable alternatives. Identify repeated calculations, costly filtering, and error-handling issues, then explain changes and recommend ways to test them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing slow calculations in Power BI reports
• Making complex DAX formulas easier to read
• Improving division and blank-value handling

Plan Power BI reports around business goals and audience needs. Get recommendations for chart types, page layouts, filters, navigation, mobile views, and accessibility, along with design specifications and a framework for reviewing usability.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan an executive dashboard with key metrics
• Choose charts for comparisons and trends
• Improve filters and navigation to detailed data

Diagnose slow Power BI reports, data refreshes, and queries through structured measurement and troubleshooting. Get recommendations for data models, DAX calculation formulas, report layouts, and infrastructure, with guidance for validating improvements and ongoing monitoring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnose slow report pages and visual interactions
• Investigate data refresh delays
• Review slow DAX calculations and queries

Review a Power BI data model’s tables, relationships, storage choices, performance, maintainability, and security. Use the findings to identify design issues and build a prioritized improvement plan with concrete recommendations and implementation guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a Power BI model before production release
• Investigating slow queries and oversized models
• Checking table relationships and storage choices

Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Grouping observed mistakes into failure categories
• Choosing which evaluation tests to build first

Set up Phoenix tracing for LLM applications in Python or TypeScript. The skill guides automatic and custom recording of individual operations, conversation grouping, metadata, feedback, and production settings such as batching and personal data masking.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up tracing for an LLM application
• Record document retrieval and tool calls
• Group conversation traces by session

Turn documents into a growing Markdown wiki and a knowledge graph, a map of entities and their relationships. Preserve source text, update linked pages as new material arrives, and answer questions with supporting evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a reusable knowledge base from documents
• Trace relationships between terms and topics
• Answer questions with source evidence

Plan and explain Lakehouse solutions in Microsoft Fabric, a place to manage tables and files together. Understand data organization, links to data without copying, access controls, and table maintenance options for designing and improving a solution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing an introduction to Fabric Lakehouse
• Planning how to organize tables and files
• Reviewing data links and access controls

Polish an existing prompt file by improving its wording, structure, and organization. Correct spelling, grammar, and unclear instructions while preserving the original intent, front matter, encoding, and Markdown structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Polishing a prompt before sharing it
• Clarifying instructions without changing their intent
• Reviewing the order and organization of a long prompt

Review an AI workflow across five areas: prompts, use of context, tools, system structure, and safety. Receive scores, specific findings, and prioritized actions to address quality and reliability problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check an AI workflow before production use
• Audit an existing agent for quality and reliability
• Prioritize fixes for workflow problems

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Review Python and BigQuery pipelines for excessive costs, duplicate writes on reruns, and hard-to-diagnose failures. Receive a structured assessment with specific function and line references, prioritized fixes, and rough estimates of the largest cost risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check BigQuery pipeline code before production use
• Estimate job counts and cost risks in loops
• Review duplicate writes when rerunning failed jobs

Refine task prompts through questions about scope, deliverables, constraints, and success criteria. Receive an organized Markdown prompt in chat and on your clipboard, with revisions supported through the Joyride extension.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a vague request into a detailed task prompt
• Clarify deliverables and success criteria
• Organize technical and procedural requirements

Organize human review in Arize by defining label choices, numeric ranges, or free-text feedback fields. Manage review queues and reviewers, submit annotations for individual records, and apply existing labels to project spans, or individual processing records, in bulk.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Define shared labels and scoring ranges for reviewers
• Create review queues and assign reviewers
• Record feedback on individual queue items

Create and run evaluations in Arize using an LLM as the judge. Define scoring criteria, connect them to actual data fields, evaluate recorded operations or experiment results, and set up continuous scoring for new data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check answers for unsupported claims
• Score experiment results for correctness
• Continuously evaluate new production records