데이터·AI 스킬

Let a NanoClaw container agent call local models running in Atomic Chat. The skill adds an MCP server, a connection that exposes model listing and text generation as tools, while Claude continues to coordinate the work.

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• Delegate part of Claude's work to a local model
• List models available in Atomic Chat
• Generate an answer with a specified local model

Add Codex as the agent that plans tasks and coordinates tools in NanoClaw. Choose it for individual groups alongside Claude, with conversation history and session resume using a ChatGPT subscription or OpenAI API key.

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• Adding Codex as a NanoClaw agent provider
• Choosing Claude or Codex for each group
• Setting up ChatGPT or API key authentication

Add Tavily web search and URL content extraction to selected NanoClaw agent groups without an API key. Configures MCP, a protocol for connecting agent tools, checks availability, and prepares an upgrade path when the shared allowance runs out.

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• Add web search to selected agent groups
• Extract structured information from URLs
• Start using Tavily without an API key

Connect a container agent to local Ollama models through MCP, a protocol for agent tools. The agent can list models and generate responses, with optional tools to download, delete, inspect, and check loaded models.

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• Delegate response generation to a local model
• Check which Ollama models are installed
• Download or delete models through the agent

Analyze CSV files with Python and get concise numerical summaries. The skill checks column structure before calculating requested aggregates, includes units when available, and explains assumptions when data is missing or incorrectly formatted.

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• Quick summaries of CSV data
• Calculating requested aggregates
• Reporting assumptions about missing or malformed data

Create or improve AI prompts through a guided conversation about goals, roles, examples, and output formats. Receive a complete, reusable prompt with an explanation of its design, including behavioral boundaries for public-facing AI roles when needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving an existing AI prompt
• Designing prompts for product naming
• Defining boundaries for a customer service bot

Connect an AI agent client to Memory Proxy, which adds team memory, skills, and knowledge to model requests. The guided workflow checks existing settings, tests connectivity, and writes the appropriate client configuration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect an AI agent to team memory
• Reuse an existing proxy configuration
• Test connectivity before writing settings

Install and configure the memory-tencentdb plugin for OpenClaw to retain and retrieve conversation information. Verify the setup through logs and searches, adjust memory settings, and troubleshoot missing recall or vector search results.

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• Enable long-term memory in OpenClaw
• Adjust recall and retention settings
• Investigate saved memories that cannot be retrieved

Maintain an application's supported language models by checking provider availability, adding models, and keeping documentation, live tests, and model-specific request settings aligned. It also checks provider model identifiers and candidates for the recommended list.

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• Add support for a specific language model
• Check whether listed models are still available
• Update provider model identifiers

Design and audit an agent's system prompt, the instructions that guide its work. Define a concrete outcome, starting input, available tools, completion checks, fallback behavior, and final report so the prompt describes a complete run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing the core instructions for a new agent
• Auditing a prompt before saving it
• Defining behavior when tools or permissions are missing

Design a general-purpose assistant for tasks such as email summaries, short reply drafts, and note capture. The playbook defines task boundaries, completion criteria, and concise responses, with only the tools needed for the requested work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing an assistant for email summaries
• Building a helper for short reply drafts
• Defining the scope of a note-taking assistant

Design research agents that search, read, and turn web or internal sources into clear reports. The playbook defines source freshness, citations, search limits, and a consistent format that separates findings from information the agent could not verify.

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• Design an agent for competitive research
• Create a brief-writing agent with dated sources
• Define citation rules for internal document research

Build a local interactive interface for exploring calculations, simple simulations, and weighted comparisons. Adjust inputs to see how results change, with visible formulas, units, and assumptions that explain what the numbers mean.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring how input values change a calculation
• Comparing options with adjustable scoring weights
• Estimating capacity with a simple worker model

Define product KPIs, design dashboards, and analyze retention, feature adoption, and conversion. Choose metrics suited to each product stage, compare groups of users over time, and turn meaningful changes into concrete product actions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing KPIs for each product stage
• Designing dashboards for different audiences
• Comparing retention across signup groups

Plan and evaluate product A/B tests with clear hypotheses, success metrics, sample size estimates, and stopping rules. Rank proposed experiments and interpret results using effect sizes and uncertainty to support practical product decisions.

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• Turn a product improvement idea into a testable hypothesis
• Estimate the sample size needed for an A/B test
• Rank experiments by impact, confidence, and ease

Design a RAG pipeline that retrieves documents to support AI answers. Analyze document splitting, select embedding models and vector databases, and evaluate retrieval quality using test queries and known relevant documents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a RAG system for internal documents
• Choosing how to split documents for search
• Comparing embedding models and vector databases

Design systems where multiple AI agents share work, generate tool schemas that define tool inputs for Anthropic and OpenAI, and analyze execution logs. Outputs include role assignments, communication diagrams, and recommendations for addressing cost, latency, and failures.

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• Designing agent roles for research automation
• Choosing how multiple agents coordinate work
• Generating and validating tool schemas

Design systems that collect, transform, and store data. Choose between scheduled and continuous processing, generate workflow configurations, validate data quality, and analyze performance using guidance for tools including Python, SQL, Spark, Airflow, dbt, and Kafka.

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• Designing data ingestion and transformation pipelines
• Choosing scheduled or real-time data processing
• Checking data freshness, completeness, and uniqueness

Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an object detection pipeline
• Clean images and convert annotation formats
• Optimize a trained model for deployment

Improve prompts through test cases and baseline comparisons, measure how well AI answers use retrieved sources, and check agent configurations. Includes Python tools for prompt analysis, retrieval quality reports, and token estimates, with costs calculated from supplied rates.

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• Compare prompt revisions against a baseline
• Measure answer quality against retrieved sources
• Check agent tool settings and loop risks

Plan and analyze A/B tests, prepare tabular data, and train and evaluate prediction models. Estimate intervention effects from observational data, track model experiments with MLflow, and translate statistical findings into business decisions.

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• Planning A/B test sample sizes and analyzing results
• Preparing tabular data for prediction models
• Comparing model performance and recording experiments

Prepare trained machine learning models for production with deployment, monitoring, and retraining workflows. Build RAG systems that retrieve documents for answers, and integrate language model APIs with retries, fallback providers, and cost tracking.

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• Deploying a trained model to production
• Setting up model monitoring and retraining workflows
• Building a RAG pipeline for document-based answers

Design workflows in which AI agents handle multiple steps or specialist tasks. Choose a suitable structure, generate starter configurations, and define information handoffs, retries, validation checks, and limits on cost and context size.

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• Choosing between one agent and a multi-agent workflow
• Generating a starter configuration for a multi-step pipeline
• Defining information handoffs and error recovery rules

Review data strategy plans through six focused questions covering training data permissions, infrastructure choices, data asset value, and hiring. Produce a structured verdict with unresolved issues and three concrete next steps.

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• Review customer data permissions before AI training
• Compare data architecture options
• Assess data assets before productization or M&A