「detect」の検索結果

Handle CSV files from construction software exports. Auto-detect delimiters, encodings, and clean messy data.

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 construction site photos to track progress, detect safety issues, and compare against BIM models using computer vision.

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Monitor construction progress using computer vision. Analyze site photos and drone imagery to track work completion, detect safety issues, and compare against BIM models.

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Aggregate and analyze IoT sensor data from construction sites. Collect data from multiple sensor types, detect anomalies, and trigger alerts for safety and quality monitoring.

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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.

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Takes meeting transcripts, extracts action items with owners and deadlines, detects implicit commitments, generates structured meeting summaries, and outputs task files compatible with Linear, GitHub Issues, and other project management tools.

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Mine lost deal notes to identify recurring objection patterns. Create objection response playbooks from won deals.

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Structured deal assessment using MEDDIC, BANT. Risk scoring, required evidence by stage, red flag detection, coaching points.

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Detect domain from context and find appropriate team member

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

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Detects shared stack membership and iterates a command across all PRs in base-to-tip order. Use when a command supports --stack flag for multi-PR iteration.

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Detects time and space complexity hotspots via AST scan. Use when code feels slow, before performance-sensitive merges, or to find O(n²) regressions.

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Detects git forge (GitHub/GitLab/Bitbucket) and maps CLI commands cross-platform. Use when writing skills that must run on any git hosting provider.

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Provides sem semantic-diff detection, install-on-first-use, and fallback patterns. Use when building skills that consume git diff output.

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Analyze and improve the improvement process. Use for detecting regressions and meta-optimization.

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Audit Skill() refs; detect hubs, isolates, and dangling targets. Use when auditing skills.

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

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Use when building, auditing or freezing a journal submission package from the canonical manuscript. Detects drift between the source and the per-journal submission copy, builds byte-preserving packages with manifests, and records current, stale or frozen status.

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Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.

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Use when clinical data may contain PHI and must be de-identified before any LLM-assisted analysis. A local Python script (no network or AI calls) detects identifiers with regex and heuristics in 11 country locale packs, with interactive terminal review.

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Use when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.

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Tạo AI Skill mới từ ý tưởng hoặc quy trình công việc. Kết hợp phỏng vấn thông minh, pattern detection, và quantitative evaluation để tạo skill chất lượng production. Dùng khi user nói "tạo skill", "biến quy trình thành skill", "make a new skill", "tự động hóa công việc này", hoặc mô tả quy trình lặp lại mà họ muốn AI tự làm. Cũng dùng khi user muốn cải thiện, test, hoặc optimize skill hiện có.

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Operate the legacy Frustum PointNets code release for KITTI or SUN RGB-D data preparation, TensorFlow runtime setup, training, inference, and 3D detection evaluation.

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Routes FCOS object-detection repo tasks for inference demos, config/data setup, training/evaluation, ONNX export, and legacy PyTorch internals.

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。