「orr」の検索結果

Manage construction punch lists for project closeout. Track deficiencies, assign corrections, and monitor completion status.

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。

AI-powered construction defect detection using computer vision. Identify cracks, spalling, corrosion, and other defects in concrete, steel, and building components from images and video.

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。

Aggregate and analyze risks across construction project portfolio. Identify correlated risks, systemic exposures, and portfolio-level risk mitigation strategies.

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。

Use the OpenConstructionERP validation engine: rule packs (BOQ quality, DIN 276, NRM, GAEB, MasterFormat, DPGF) that check estimates at import time and on demand. Use when verifying BOQ correctness or writing custom validation rules.

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。

MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.

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。

Answers business questions about the user's own spreadsheet or data export (CSV, TSV, XLSX from a CRM, Shopify, Stripe, QuickBooks, ad platforms, HR or payroll systems) correctly and auditably. Profiles every file first (grain, duplicate keys, embedded Grand Total rows, text-stored numbers, mixed currencies, UTC timestamps, Excel serial dates, hidden rows), states the metric definition used, computes every number in SQL or pandas instead of mental math, checks joins for fan-out, reconciles to a known total, and returns the query, filters, row counts, and rows behind the answer. Use this whenever the user says "analyze this spreadsheet", "how many...", "what's the total in this export", "which customers...", "pivot this", "revenue by month", "top 10", "what's our churn/MRR/AOV", or asks any question about their data, CSV, or Excel file, even a quick one. Also use when they ask to change values in a data file.

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。

Converts bank, credit card and brokerage statement PDFs, invoices and vendor price lists into clean CSV or Excel, then proves the extraction is complete and correct - opening balance plus transactions equals closing balance, every running balance chains, page totals, row counts and summary totals all tie, signs and decimal separators are consistent. Handles digital PDFs with word-position column bands and detects scanned PDFs that need OCR first. Use it whenever the user wants to convert a bank statement PDF to Excel or CSV, extract transactions from a statement, turn a PDF table into a spreadsheet, import statements into QuickBooks or Xero, or says the converted statement does not balance, rows are missing, or signs look wrong, even if they do not ask for a proof. Never reads amounts off the page image when a text layer exists, and never fixes a failed tie-out by guessing.

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。

Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.

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。

Fixes broken decks/PDFs exported from Claude's "Design" feature (or similar AI deck generators) — cut-off or clipped text, wrong/substituted fonts, and corrupted .pptx package structure that shows up only once you export to PDF, not while looking at it in the app. Use this skill whenever the user uploads a zip, .pptx, or .pdf exported from Claude Design (or mentions "Design" export, an AI-generated deck, or a generated slide deck/PDF) AND reports it looking broken, wrong, cut off, garbled, or different from the original when opened, printed, or converted to PDF — even if they don't use the word "repair" or name the specific defect. Also trigger for general "my exported PDF/deck is broken, help fix it" requests when the file was clearly produced by an AI design/deck tool rather than hand-authored. Produces both a repaired, still-editable .pptx and a clean PDF, plus a report of exactly what was wrong and what changed.

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。

Performs a deep code review of files, modules, a diff, or a branch - finding security vulnerabilities (mapped to OWASP Top 10:2025), correctness bugs, performance problems, and maintainability issues - and returns severity-ranked findings with evidence and concrete fixes. Use when the user asks to review, audit, or sanity-check code, asks "is this safe", "what's wrong with this", "find bugs", or wants a security or performance pass before shipping. For posting line comments on a GitHub pull request, use git-pr-reviewer.

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。

Scan documents and slides for off-brand colors, fonts, and logos. Validate against brand guidelines and suggest corrections.

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。

How to query a Well workspace correctly — discover the schema first, then query the right root. Use whenever the user asks about their invoices, companies, contacts, bank transactions, accounts, or accounting ledger in Well, or before writing any well_query_records call.

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。

Start here for any iOS or SwiftUI task. Coordinates best-practice guides, correctness checks, and full Apple API references. Use before navigating to other Apple skills — for building, reviewing, refactoring, or debugging iOS apps.

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。

Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization 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。

Updates documentation after code changes with quality gates, slop detection, and accuracy checks. Use when code changes require corresponding doc updates.

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。

Audits shell scripts for correctness, portability, and common pitfalls. Use when reviewing shell scripts or before committing shell changes.

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。

Verifies math-heavy code for algorithmic correctness and numerical stability. Use when reviewing scientific algorithms, ML models, or numerical code.

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。

Verifies workspace state and staged changes as a read-only preflight. Use before commits or PRs to confirm staged set is clean and correct.

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。

Audits Makefiles for build correctness, portability, and recipe duplication. Use when reviewing a Makefile or before committing Makefile changes.

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。

Recovers broken agent state via crash recovery, context overflow, and merge conflict protocols. Use when an agent session fails or a worktree is corrupted.

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。

Orchestrates full project lifecycle by auto-detecting state and routing to the correct phase. Use when starting or resuming a project mid-workflow.

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。

Use when validating or evaluating a trained medical-imaging model. Audits split leakage and validation design, computes task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), and covers uncertainty/OOD and Grad-CAM explainability, each with a gate.

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。

Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.

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。

Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.

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。