「compute」の検索結果

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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Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.

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Previews, provisions, or diagnoses a uv-managed local Python .venv with `databricks environments setup-local`. Use when the user wants to set up or fix one for Databricks Connect, cluster or serverless compute, `--job-task`, or a bundle target, or when setup-local fails.

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Computes DORA delivery-performance metrics from git and GitHub API. Use when assessing deployment frequency, lead time, or change failure rate.

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Builds estimates, quotes, and bids for trades and field-service businesses (HVAC, plumbing, electrical, roofing, landscaping, painting, cleaning, remodeling, general contracting, IT/AV installs). Turns a scope into line items with waste factors, burdened labor, materials, equipment, subs, permits, mobilization, contingency, overhead, profit, and tax, then writes a client-ready xlsx (live formulas) and PDF plus an internal cost sheet, with good/better/best options, add-ons, a payment schedule, exclusions, and assumptions. Use whenever someone asks for a quote, estimate, bid, proposal price, or job price, asks "how much should I charge for this job", wants to check a price or margin on a job, or is confused about markup vs margin. Computes deterministically with a script instead of mental math, because models routinely treat a 30% markup as a 30% margin and drop labor burden, waste, and mobilization.

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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.

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Drop a folder of vendor quotes, proposals, and SOWs on Cowork -- normalizes them into one comparison matrix, computes true total cost of ownership, surfaces the terms each vendor buried, and arms you with negotiation leverage points and reference-check questions.

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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.

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Online (incremental) covariance, correlation, and precision estimation in Python — the streaming complement to sklearn.covariance. Use when code needs a covariance/correlation matrix updated per observation, recomputes np.cov/np.corrcoef in a rolling loop, must judge or compare covariance estimates, or proposes a new covariance methodology. Points to task-specific skills.

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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.

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Use Fairlearn to assess group fairness, compute disparity metrics, visualize subgroup performance, and mitigate unfairness with preprocessing, reductions, postprocessing, or adversarial learning.

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Use Hugging Face Evaluate to load metrics, comparisons, and measurements; compute and combine results; run evaluator pipelines; create custom modules; troubleshoot optional dependencies, cache, Hub, and CLI workflows.

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Operate CVAT for computer-vision annotation, dataset workflows, SDK/CLI automation, auto-annotation, and self-hosted deployment.

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Manage EC2 instances, AMIs, and auto-scaling groups. Configure security groups, key pairs, and instance types. Use when deploying compute resources on AWS.

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Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

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Applies the reasoning style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

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Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.

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Render the running viewer in a real headless browser and assert on what it drew — element geometry, computed colours, wrap points, truncated labels, console errors. Use when a change moves pixels (navbar/badge/chart layout, CSS, responsive behavior, theme), when a PR says it "needs a visual check", or when reviewing frontend work under src/viewer/assets/. Covers the gap viewer-smoke names: it asserts payload shape, this asserts rendering.

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Debug Fastly Compute services where code changes don't affect behavior due to multi-service routing architecture. Use when: (1) deployed changes have no effect, (2) unexpected redirects persist after code updates, (3) DNS resolves to Fastly but responses don't match deployed code, (4) `fastly service list` shows multiple related services. The root cause is often a routing/gateway service intercepting requests before they reach your content service.

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Fix incorrect priority ordering when using denormalized aggregate columns. Use when: (1) Records are processed in wrong order despite ORDER BY on count/sum columns, (2) Top items by some metric aren't being selected first, (3) Aggregate columns show 0 or NULL for records that should have high values, (4) Priority queue processes low-value items before high-value ones. The root cause is often that denormalized columns (vine_count, loop_count, total_orders, etc.) weren't backfilled or maintained properly. Solution: JOIN with source tables to compute actual aggregates at query time.

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Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.

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