Manage construction change orders from request to approval. Track costs, schedule impacts, and maintain audit trail for dispute prevention.
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概要と使いどころ
Manage construction change orders from request to approval. Track costs, schedule impacts, and maintain audit trail for dispute prevention.
日本語の概要は準備中です。原文の説明を表示しています。
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze historical construction costs for benchmarking, trend analysis, and estimating calibration. Compare projects, track escalation, identify patterns.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Track material procurement from requisition to delivery. Monitor lead times, vendors, and costs.
日本語の概要は準備中です。原文の説明を表示しています。
Process and manage construction change orders. Track costs, approvals, and impact on schedule and budget.
日本語の概要は準備中です。原文の説明を表示しています。
Track and analyze historical cost data using CWICR. Compare actual vs estimated costs, build project cost database, and improve future estimates.
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Apply overhead, profit, and markup to CWICR estimates. Calculate indirect costs, general conditions, and contractor margins.
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Analyze budget vs actual cost variances. Identify overruns, forecast final costs, and generate variance reports.
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Apply geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions.
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Analyze and predict construction change orders using ML. Classify change order types, predict costs and schedule impacts, identify patterns, and optimize approval workflows.
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Access and utilize open construction pricing databases. Match BIM elements to standardized work items, calculate costs using public unit price databases with 55,000+ work items.
日本語の概要は準備中です。原文の説明を表示しています。
Connect OpenConstructionERP to AI coding assistants via MCP (Model Context Protocol): expose costs, BOQ, BIM and catalog endpoints as MCP tools so Claude Code / Antigravity / OpenCode can drive the platform. Use when wiring an assistant to the ERP.
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Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
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Designs pricing strategies for products and services. Takes product/service, costs, target market, competitors. Analyzes cost-plus, value-based, competitor-based, penetration, premium models. Researches competitor pricing. Generates pricing-strategy.md with recommended model, price points, tier structure, discount policies, annual vs monthly analysis, sensitivity to churn, expansion revenue modeling.
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Evaluates which SaaS tools can be replaced with AI agents. Takes a list of current SaaS subscriptions with costs, assesses replacement feasibility, estimates build vs buy economics, identifies Claude+MCP alternatives, and generates a comprehensive replacement plan with priority matrix, ROI analysis, implementation timeline, and risk assessment.
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Calculate comprehensive ROI for AI implementation projects. Takes current costs, manual process time, team size, and hourly rates. Generates detailed roi-analysis.md with executive summary, cost-benefit tables, sensitivity analysis, break-even timeline, and comparison scenarios. Use when evaluating AI investments, building business cases, or justifying automation spend.
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Find out which clients and jobs actually make money -- allocate real costs (labor hours, materials, subs, overhead) against what each job billed, and rank every client by true margin. Most service businesses discover their biggest client is their least profitable.
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Build an insurance-grade home inventory from a folder of photos and receipts -- identifies items, pulls values from receipts, estimates replacement costs, organizes by room, and outputs the documentation an insurance claim actually requires. Update mode keeps it current after new purchases.
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Read huge, dense, read-only text as rendered PNG pages instead of raw text — image tokens scale with pixels, not characters, so token-dense bulk (big files, vendored code, diffs, logs) costs ~60–75% less as an image. Use when you must skim or reason over thousands of lines you will NOT edit or byte-copy. Lossy on exact strings: never for files you'll Edit, secrets, hashes, or byte-exact values. Fable-class readers only.
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Rewrite a memory or context file (CLAUDE.md, AGENTS.md, a todo or notes file) into Honey-terse form so it costs fewer input tokens every session, without losing meaning. Backs up the original first. Use when asked to shrink or compress context files, trim CLAUDE.md, or cut per-session input cost. Prose only — never code, config, or data.
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AWS cost optimization, monitoring, and operational excellence expert. Use when analyzing AWS bills, estimating costs, setting up CloudWatch alarms, querying logs, auditing CloudTrail activity, or assessing security posture.
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Automated cleanup of unused AWS resources to reduce costs
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
日本語の概要は準備中です。原文の説明を表示しています。