데이터·AI 스킬

Prepare market history for pattern detection and comparison. Import opening, high, low, and closing prices plus volume, validate and normalize the values, then store them by symbol and date for later retrieval. It does not place orders.

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• Loading price and volume history for a symbol
• Preparing market data for pattern detection
• Normalizing data before comparing price movements

Define reusable reasoning and decision patterns for agents, embed them in executable workflows, and review their performance. Share successful approaches, including conditional decision trees, stepwise problem solving, analogies, and evaluation from multiple perspectives.

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• Define conditional rules for agent decisions
• Turn problem-solving steps into executable workflows
• Solve new problems using similar past cases

Inspect evolved prompt configurations, validate their structure, and view the system prompts they produce. You can also classify failures in run transcripts to support reviews before adopting or changing an agent policy.

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• Reviewing the system prompt before adopting an evolved policy
• Validating prompt configuration changes in CI
• Classifying failures in an agent run transcript

Create agents that learn from interactions and adapt their behavior over time. Monitor learning progress, review efficiency and accuracy metrics, trigger adjustments from feedback, and share acquired knowledge with other agents through dedicated tools.

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• Create an agent with initial learning settings
• Monitor learning progress, efficiency, and accuracy
• Adjust agent behavior based on feedback

Send a single request to DeepSeek for summarization, information extraction, or simple classification. Receive the response and token usage as JSON for further processing, with structured status information when the API is unavailable.

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• Summarizing text in a single request
• Extracting information from text
• Classifying text into simple categories

Evaluate and improve the policy instructions that guide an AI agent using scored coding tasks. Run a learning cycle to compare candidates, or use the default dry run to inspect tasks and estimated costs without model calls.

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• Improving agent policies against a scored task set
• Estimating learning costs before running models
• Comparing candidates on held-out evaluation tasks

Send proofs, planning questions, or difficult debugging tasks to DeepSeek's reasoning model. Receive the final answer and model-provided reasoning text in separate fields, with token usage reported for reviewing responses and costs.

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• Requesting a mathematical proof
• Planning work that requires several reasoning steps
• Investigating root causes of difficult bugs

Choose an agent and model tier using the task description and learned patterns. The skill can explain its recommendation, use compiler-based edits for supported simple changes, and record task outcomes to inform future routing decisions.

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• Choose an agent and model before starting a task
• Review the reasoning behind a routing recommendation
• Apply supported code changes through the TypeScript compiler

Reuse patterns learned in another local project, or find, download, and publish patterns through an IPFS sharing registry. It supports confidence filters for local transfers and verification checks when downloading shared patterns.

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• Reuse learned patterns in a new project
• Find and inspect shared patterns
• Publish patterns for peers to reuse

Assess tasks before execution to identify transformations eligible to skip LLM calls. Report the eligible share, recommended models for remaining tasks, and estimated upper costs, then save the routing results for later cost reports.

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• Assess similar tasks before a batch run
• Review LLM spending on simple code transformations
• Check the share eligible for Agent Booster

Compare actual AI session costs with estimates for using only Haiku, Sonnet, or Opus. The skill reports savings amounts and percentages, helping teams assess model selection costs while leaving differences in answer quality and retries outside the calculation.

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• Report model selection savings during cost reviews
• Check savings after changing model selection settings
• Use JSON results to enforce a savings threshold in CI

Identify individual sessions with unusually high or low spending using the median and MAD, a measure of spread around it. Filter by time period, adjust detection thresholds, and optionally return a failure exit code for automated checks.

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• Find sessions with unusually high spending
• Check sessions with unusually low spending
• Review spending anomalies over the past week

Retrieve compacted ReasoningBank context for cost-analysis queries and summarize retrieved memories, reported token savings, cache hit rate, and package availability. The savings figure is an estimate from the integration, not a measured comparison against a baseline.

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• Retrieve compacted context for a cost-analysis query
• Review retrieved memory counts and cache hit rate
• Inspect reported token savings and their limitations

Analyze the last seven days of agent and model token usage to identify unnecessary spending. Recommend changes to model selection, prompt caching, and overlapping work, with estimated dollar savings, percentage reductions, and implementation priorities.

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• Investigating higher-than-expected AI costs
• Matching model choices to task complexity
• Reviewing prompt caching efficiency

Generate a spending report from recorded AI usage, with token counts and USD costs grouped by model, agent, and processing tier. Check budget utilization, remaining funds, and alerts for a selected period.

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• Reviewing today's AI spending
• Comparing costs across models and agents
• Checking budget utilization and alerts

Check actual OpenRouter charges, API key limits, daily, weekly, and monthly spending, and account credits. Look up a single request by generation ID and report the billed model and provider returned by OpenRouter.

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• Check daily, weekly, and monthly OpenRouter spending
• See whether an API key is nearing its limit
• Look up a request's billed cost by generation ID

Review AI agent spending by conversation, with each session’s start time, message count, main model, and total cost. Use the results to identify expensive sessions and understand typical costs before planning longer work.

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• Review spending across completed conversations
• Identify conversations with high costs
• Estimate typical costs before a long session

Estimate the cost per million tokens, units of model text, on hardware you own using power consumption, electricity rates, hardware costs, and measured generation speed. Compare utilization levels to find when local operation costs less than a hosted model.

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• Compare local model costs with a hosted API
• Estimate costs at different utilization levels
• Include electricity and hardware depreciation in costs

Break down a Claude Code session's costs by assistant message to find expensive responses. Compare input, output, cache writes, cache reads, and models, with message cost percentiles providing context for investigating unusually costly activity.

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• Investigate messages in a flagged session
• Check the cost contribution of cache writes
• Review expensive messages after a specific time

Extract text, tables, and attributes from rendered web pages into structured data. Check extracted content for personal information and malicious instructions, and save reusable extraction templates for pages on the same host.

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• Extracting text and tables from rendered web pages
• Saving templates for recurring extraction tasks
• Applying a saved template to another URL on the same host

Delegate complex work through a tree of Claude Code sub-agents. Each level uses its own working context and returns concise summaries, helping the coordinating agent manage research, verification, and synthesis without retaining every intermediate detail.

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• Divide research, expansion, verification, and synthesis into layers
• Handle work with more intermediate detail than one agent can retain
• Assign specialized checks to different leaf agents

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

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• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Build agents that record task outcomes and use past experience to choose future approaches. Implement pattern matching, strategy comparisons, persistent learning records, and metrics for checking how recommendations and results change over time.

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• Build an agent that learns from task outcomes
• Compare approaches to code review or bug fixing
• Find learned patterns relevant to the current situation

Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.

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• Train an image classification model
• Forecast time series with LSTM
• Deploy a sentiment analysis template