Data analysis, machine learning, LLM apps, and prompt design (9.2K skills)
Search and extract contact information for people or companies including names, phone numbers, emails, job titles, and LinkedIn profiles. Aggregates data from multiple sources and provides enriched contact details. Use when users need to find contact information, build prospect lists, or enrich existing contact data.
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This skill should be used when the user wants to record bookkeeping entries (仕訳), import transaction data from CSV files, receipts, or invoices, or manage their general ledger. Trigger phrases include: "仕訳を入力", "仕訳登録", "CSVを取り込む", "レシートを読み込む", "請求書を取り込む", "帳簿を付ける", "経費を記録", "売上を記録", "仕訳を修正", "仕訳を検索", "仕訳を削除", "取引を登録", "帳簿の初期化".
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Background context for the Electronic Bookkeeping Act (電子帳簿保存法) in the shinkoku tax filing plugin. Contains requirements for electronic bookkeeping, scanner storage, mandatory electronic transaction data storage, and shinkoku's compliance status. This skill is not user-invocable — Claude loads it automatically when responding to electronic bookkeeping compliance questions.
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This skill manages furusato nozei (hometown tax) donations. Use when the user wants to register donation data, read donation receipts, check deduction limits, or manage their furusato nozei records. Trigger phrases: "ふるさと納税", "furusato", "寄附金", "寄付金", "ふるさと納税の控除", "寄附金受領証明書", "ワンストップ特例".
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Use when writing a complete SEO article. Includes the full anti-AI-slop ruleset (banned vocabulary, banned phrases, banned structural patterns) and voice rules. The agent researches the SERP itself if needed — no keyword data exports required.
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Use when rewriting or refreshing an existing page that's underperforming. The agent fetches the URL, analyzes the current content, researches the SERP, and rewrites using the full anti-AI-slop ruleset — no data exports needed.
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Comprehensive verification system for construction automation deliverables. Use after completing estimates, schedules, reports, or data processing tasks to ensure quality.
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Curated few-shot examples for construction AI tasks: classification, extraction, analysis. Domain-specific examples for improved LLM performance.
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Validate construction data inputs before processing: cost estimates, schedules, BIM data, field reports. Catch errors early with domain-specific rules.
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Assess company readiness for construction industry uberization. Analyze data transparency, process automation, and competitive positioning against open data platforms.
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Reusable prompt templates for construction AI tasks: cost estimation, schedule analysis, document processing, BIM queries. Structured prompts for consistent results.
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JSON/data schema validation for construction data exchange: API payloads, file imports, BIM exports. Ensure data structure compliance before processing.
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Check data compliance with construction standards. Validate data against ISO 19650, IFC, COBie, UniFormat standards.
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Design data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.
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Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.
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Extract data from construction images using AI Vision. Analyze site photos, scanned documents, drawings.
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Build KPI dashboards for construction projects. Track CPI, SPI, quality, safety metrics in real-time.
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Automated pipeline for retraining ML models with new construction data. Monitor model drift, trigger retraining, and validate model performance.
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Build ML models for construction predictions. Train and evaluate custom models for cost, duration, and risk prediction.
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Analyze large-scale construction datasets. Process thousands of projects for patterns, benchmarks, and predictive insights.
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What-if analysis for construction projects: model different scenarios and their cost/schedule/resource impacts. Compare alternatives and optimize decisions.
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Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting.
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Extract and analyze data from construction ERP systems. Pull project data for analytics, reporting, and integration.
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Calculate embodied carbon and lifecycle emissions for construction materials and projects. Support sustainable design decisions with carbon data.
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