数据分析、机器学习、LLM 应用、提示词设计（9018 个）
Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.
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Extract and analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.
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Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
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Cleans up CRM data from HubSpot, Salesforce, Pipedrive, Zoho, Close, Attio, or any CRM CSV export. Finds duplicate contacts and companies with fuzzy matching (not just exact email), normalizes emails, phones to E.164, names, domains, states, and countries, flags stale, invalid, role, and test records, finds broken contact-company associations, and produces a reviewable merge plan plus import-ready files without touching the live CRM. Use this skill whenever the user wants to dedupe, find or merge duplicate contacts, merge companies or accounts, clean up a CRM, prep a CRM migration or re-import, or says the CRM export is a mess. Also use it when they mention only one symptom, like "the same person is in here three times", "half our leads are junk", or "our company records don't match".
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Prepare YOUR data room for a fundraise, acquisition, or bank diligence -- builds the checklist for your deal stage, sweeps your folders for what you already have, produces a gap report, and organizes everything into the structure buyers and investors expect. The sell-side complement to cowork-deal-room.
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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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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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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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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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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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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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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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Predict project completion dates using ML models. Forecast schedule delays based on current progress, historical patterns, and risk factors.
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Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.
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Design data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.
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Check BIM data against IDS (Information Delivery Specification). Validate model information requirements and compliance.
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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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Automated pipeline for retraining ML models with new construction data. Monitor model drift, trigger retraining, and validate model performance.
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