「project」の検索結果

Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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Draft Project Manual specification sections from accepted project requirements using CSI MasterFormat identities, three-part sections and review flags. Use for design requirements; use product-cut-sheet for source-faithful product entries and epd-to-spec for EPD requirements.

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Guided, hands-on course teaching architects how to use Codex or Claude Code — six short modules, each built around an exercise on a bundled sandbox project (a fictional Brooklyn art museum expansion). Resumable across sessions via PROGRESS.md. Use when the user runs $learn or /as:learn, says they're new to AI-assisted project work, or asks how to learn it.

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Draft Project Manual specification requirements for EPD submittals and sourced GWP limits. Use for embodied-carbon requirements in project sections; use the EPD parsing, research or comparison skills for product evidence.

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Retrieve mouse phenotype data from the Jackson Laboratory Mouse Phenome Database (MPD) via its REST API. Browse 520+ projects, look up per-project measure metadata, pull strain-level means (raw or LS-mean adjusted) and per-animal values, find measures by MP/VT ontology terms, and resolve strain nomenclature or gene coordinates. Use for QTL support, cross-strain comparison, mouse model selection, and ontology-driven phenotype discovery. Use monarch-database for disease-gene-phenotype knowledge graphs; ensembl-database for mouse genome annotations.

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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Provides AWS SAM bootstrap patterns: generates `template.yaml` and `samconfig.toml` for new projects via `sam init`, creates SAM templates for existing Lambda/CloudFormation code migration, validates build/package/deploy workflows, and configures local testing with `sam local invoke`. Use when the user asks about SAM projects, `sam init`, `sam deploy`, serverless deployments, or needs to bootstrap/migrate Lambda functions with SAM templates.

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Canonical rules for writing, rewriting, or reviewing Shift desktop Playwright E2E tests and visual goldens under `apps/desktop/e2e/`. Use whenever you add or change a `.spec.ts`, a fixture, `EditorDriver`, a screenshot baseline, or Playwright project membership, and whenever you investigate a flaky or failing E2E test. Covers waits, oracles, goldens, fixtures, projects, and flake verification.

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Forecast construction project cash flow. Project income and expenses, identify funding gaps, and optimize payment timing for improved financial management.

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 project duration using k-NN and regression. Estimate timeline based on similar historical projects.

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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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Manage project closeout activities. Track completion of documentation, warranties, and final inspections.

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Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.

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Forecast project cash flow based on schedule and cost data. Generate S-curves and payment projections.

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Integrate CWICR cost data with project schedules. Link work items to schedule activities, generate cost-loaded schedules, and cash flow projections.

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。

Apply machine learning for construction project risk assessment. Predict schedule delays, cost overruns, and safety incidents using historical data and project characteristics.

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Multi-project portfolio analytics dashboard. Aggregate KPIs across projects, track portfolio health, compare performance, and support executive decision-making.

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AI-powered contractor matching and selection for construction projects. Analyze contractor capabilities, past performance, certifications, and project requirements to recommend optimal matches.

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Portfolio mapping and model coordination in OpenConstructionERP: the 3D geo hub (Cesium), the coordination hub with clash AI, and geo-anchored projects. Use for multi-project portfolio views and BIM clash workflows.

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Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.

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Create interactive KPI dashboards for construction projects. Track schedule, cost, quality, and safety metrics in real-time.

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。

Plan organizational capacity for construction projects. Forecast resource needs, identify capacity gaps, and support strategic planning for project pursuit and staffing.

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。

Generates professional Statements of Work from a project brief. Use when a user needs to create an SOW, scope a project, define deliverables and milestones, or produce a consulting engagement document.

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Create production-quality Android applications following Google's official architecture guidance and NowInAndroid best practices. Use when building Android apps with Kotlin, Jetpack Compose, MVVM architecture, Hilt dependency injection, Room database, or multi-module projects. Triggers on requests to create Android projects, screens, ViewModels, repositories, feature modules, or when asked about Android architecture 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。