데이터·AI 스킬

Turn A/B tests and product experiment records into decision reports. Organize hypotheses, setup, and metrics, explain findings and uncertainty, then recommend shipping, iterating, extending, or stopping, with follow-up experiments based only on supplied data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether to launch a tested product change
• Reviewing growth or pricing experiments
• Assessing onboarding changes or email experiments

Turn supplied CSV, Excel, or JSON data into a visual report page with key metric cards, multiple charts, and a data table. Include the reporting period, data sources, written observations, and a collapsible explanation of the methods used.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning weekly performance data into a report page
• Reviewing key metrics and year-over-year changes
• Sharing data trends with charts and supporting tables

Organize parsed documents into four color-coded cards covering facts, explanations, risks, and actions. Preserve source references and generation details so readers can review evidence, identify uncertainty, and consider next steps in research, financial, or legal analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Separating facts from interpretations in financial reports
• Organizing evidence and risks in legal documents
• Turning research documents into evidence-linked analysis cards

Turn data findings into reports and presentations that explain why the numbers matter. The skill combines context, problems, recommendations, and visual guidance to help executives and nontechnical audiences understand evidence and decide on next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explaining analysis results to executives
• Structuring quarterly business reviews
• Building investor presentation narratives

Guide an AI agent through ClawWork, an economic survival simulation where task rewards must cover API token costs. Defines a daily choice between work and learning, artifact submission, knowledge storage, and priorities based on the remaining balance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose work or learning based on the remaining balance
• Create and submit task artifacts for evaluation
• Save research findings to persistent memory

Classify messages, rate content, or estimate probabilities using defined question types. Receive machine-readable answers, with confidence scores for classification and ratings, and apply the same questions to multiple inputs in a batch.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Routing customer inquiries to departments
• Rating support quality against defined criteria
• Classifying sentiment across multiple messages

Guides the design and review of agent startup context, including role files and links to authoritative records. It helps identify stale guidance, missing updates, overloaded startup files, and instructions that lose the purpose of the work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing startup files for a new agent
• Reviewing stale guidance used by running agents
• Separating role information from reusable procedures

Helps an agent take over an existing role after a planned handover. It guides checks of inherited records, identity, authority, and recurring duties, while explaining how to recover the predecessor’s reasoning and preserve lessons for the next agent.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Getting oriented after a planned agent handover
• Checking inherited records and recurring duties
• Recovering the reasoning behind earlier decisions

Review how an AI agent loads, retains, summarizes, and shares working information. This historical guide helps examine forgotten constraints, repeated actions, tool selection, and handoffs using context engineering practices published in 2024–2025.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing information loaded at startup versus on demand
• Investigating repeated actions or forgotten constraints
• Planning summaries and persistent task notes

Diagnose why an AI agent skips tools, passes incorrect arguments, or repeats calls. Get guidance on tool descriptions, schemas that define accepted inputs, execution flow, and mock tests before connecting to a real model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding why an agent never calls a tool
• Fixing incorrect or missing tool arguments
• Investigating repeated calls or skipped steps

Draft copy-ready /goal commands for long-running tasks in Claude Code and Codex. Turn a request into a checkable outcome with verification steps and stopping conditions, while flagging missing details instead of inventing them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a goal for a long-running development task
• Clarify completion criteria for an existing goal
• Shorten a goal prompt to fit the character limit

Connect stock data to an application using Finnhub Stock API. The skill covers current quotes, company profiles, historical prices, and market news, with server-side request handling to protect API keys and manage cached responses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Display current stock prices on a dashboard
• Retrieve historical prices and trading volumes
• Show company profiles alongside stock quotes

Continue analysis after repeated tool failures by embedding known data in Python scripts run through run_shell. Save intermediate results as JSON with timestamps and source metadata, creating records that can be checked and reused in later steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Continuing analysis when search and execution tools fail
• Comparing prices using competitor data already available
• Saving intermediate results for later review

Diagnose why an AI workflow becomes inconsistent despite receiving extensive information. Through guided questions, define what to retain, retrieve when needed, or exclude, and develop a memory structure and a research-to-implementation process with focused context.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnosing vague answers despite extensive input
• Defining information boundaries between agents
• Planning persistent memory and on-demand retrieval

Plan NVIDIA NuRec workflows that turn sensor recordings into renderable 3D scenes. This routing skill selects the relevant specialist skills for dataset downloads, conversion, training, rendering, and frame cleanup, and puts the steps in order.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a reconstruction workflow from sensor recordings
• Find the steps to render an existing USDZ scene
• Choose a workflow for cleaning up rendered frames

Analyze product usage in PostHog and manage experiments, dashboards, surveys, and feature flags, which control feature availability. Use connected tools to investigate user behavior and errors, run queries, and summarize findings with numbers and links.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyze conversion funnels and user retention
• Manage feature rollouts and A/B tests
• Investigate errors and review session recordings

Coordinate CAD and source assets through conversion to USD, a 3D scene format, material and physics assignment, and SimReady checks. Produce stage reports, with preview rendering and validated delivery packages when requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare CAD assets for simulation
• Convert source assets to USD and check basic validity
• Assign materials and physics properties

Plan or run inherited DNA variant detection from whole-genome, exome, or targeted-panel sequencing. Check reference compatibility, sample quality, and combined analysis across samples, with documented commands, resource versions, and paths to variant and annotation outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning inherited DNA variant detection from sequencing data
• Preparing joint variant analysis for cohorts or families
• Checking reference resources and sequencing quality

Analyze Mixpanel data with Python to explore usage trends, conversion steps, returning users, and differences between user groups. Also manage business context documents that explain an organization's structure and goals to AI assistants.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find where users drop out before purchasing
• Compare retention across user groups
• Explore feature usage and revenue trends

Prepare single-cell or single-nucleus RNA-seq analysis by checking inputs and selecting workflows that turn FASTQ sequencing files into count matrices. Route quality checks, cell annotation, clustering, and UMAP visualization to the embedded downstream skill.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Start single-cell RNA-seq analysis from FASTQ files
• Choose a workflow for generating count matrices
• Route existing matrices to quality checks and cell annotation

Validate FASTQ sequencing files, run local quality checks, and interpret reports to decide whether trimming is needed. Optionally remove adapters or low-quality read ends while preserving original reads and their quality reports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check sequencing quality before downstream analysis
• Summarize quality reports across multiple samples
• Assess whether adapter or quality trimming is needed

Plan DNA variant detection workflows by identifying the assay and sample model, then routing to germline, cancer somatic, or molecular-barcode panel skills. It also supports focused local BAM/CRAM checks with variant files, depth summaries, and optional filters.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a workflow for WGS, WES, or DNA panels
• Routing inherited, cancer, and molecular-barcode analyses
• Checking reference genomes and required input files

Plan or run bulk RNA-seq processing from sequencing reads to count matrices, tables of expression counts across samples. Check sample metadata, reference consistency, read mapping, and quantification quality, and produce summaries for downstream analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate count matrices from FASTQ files
• Check reference files and sample metadata
• Review mapping and quantification quality

Plan or run DNA analysis to find small genetic changes in tumors, using tumor samples alone or paired with normal tissue. Check sample pairing, contamination, data quality, filtering, and annotations that add information to detected variants.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning analysis of paired tumor and normal samples
• Reviewing filtering and limitations for tumor-only analysis
• Checking quality in whole-genome, exome, or cancer-panel data