'dataset' 검색 결과

Builds a DRAFT data dictionary from a schema export, table definition, sample rows or a partial existing dictionary: one row per field with the name as stated, type as stated or observed, meaning quoted from the documentation supplied, owner, allowed values, and UNKNOWN wherever no source defines the field, plus a gap list for the data owner. Never infers a definition from a column name. Use when the user asks to "build a data dictionary for this table", "document these columns", "what does each field mean", "turn this schema export into a dictionary" or "fill in the field definitions from these documents". Do not use for profiling values or finding patterns in the data, use dataset-insight-pack instead; for metric definitions, use kpi-definition-sheet; for logging data defects, use data-quality-issue-log. Drafts for human review; never approves, authorises or signs off.

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Reads one spreadsheet, CSV export or pasted table together with the user's question and drafts an insight pack: a data profile, observations cited to named columns and values and framed as leads, caveats, and suggested charts and follow-up analyses. Every figure carries the label "as read, verify in source"; correlation is never stated as causation. Use when the user asks to "profile this spreadsheet", "what does this data say", "find anomalies in this extract", "give me a first read on this CSV" or "summarise this table". Do not use for a purchase order log or spend extract, use purchase-order-anomaly-review instead; for actuals versus budget, use budget-variance-explainer instead; for a report that arrives by email on a schedule, use report-attachment-analyzer instead. Drafts for human review; never approves, authorises or signs off.

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- Research companies, directors, shareholders and ultimate beneficial ownership in official corporate registries, filings and offshore datasets — OpenCorporates, UK Companies House and the PSC register, SEC EDGAR, US Secretary of State registries, EU business registers, GLEIF LEI… Use when asked who owns or controls a company, to find a person's other directorships, or to unpick a group structure

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Author and submit a Narrative workflow from a natural-language intent. Picks the closest example from `assets/examples/`, adapts it to the user's case, walks the YAML against the spec, resolves the data plane, and submits via `narrative_workflows_create` only after the user has approved the rendered spec. Use when: "create a workflow that does X", "schedule a daily refresh of dataset Y", "wrap this NQL as a workflow", "build a pipeline that creates view A then refreshes view B", "submit this workflow YAML", "productionize this query as a recurring job". (narrative-common)

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Load before external product/design research, competitor comparison, clean-room implementation, or adding/updating code, packages, images, fonts, datasets, generated artifacts, or other dependencies whose source/provenance/license may affect outbound licensing; not for purely internal source use with no dependency change.

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Train an OpenAmer-specific small model from the collected brain data (QLoRA on NVIDIA, CPU smoke-test on laptop). 'The organism becomes its own brain' — build a fine-tune dataset, train, eval, export.

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It took several weeks and dozens of GPUs to train the model on 40 years of historical weather data from the MERRA-2 is NASA’s harmonized dataset of sa

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Datasets : ProntoQA, FOLIO, ProofWriter, ConditionalQA, StrategyQA Model : GPT-5 (Azure deployment) Config : max_attempts=3 , verify_timeout=10000ms B

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End-to-end data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready data.

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Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.

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Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

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Automate web scraping and data extraction with Apify -- run Actors, manage datasets, create reusable tasks, and retrieve crawl results through the Composio Apify integration.

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Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.

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Use when the user asks to "build programmatic SEO pages", "generate pages at scale", "rank on a high-authority third-party site", "borrow domain authority", "build a vs / alternative page", "do local SEO", "optimize a Google Business Profile", or "fix NAP" — one page-build router with four modes: programmatic (template × dataset systems), parasite (barnacle publishing on Medium/Reddit/LinkedIn/GitHub), comparison (vs / alternative pages in four formats), and local (GBP + NAP + citations + location pages), each with its own thin/duplicate, honesty, or reputation-abuse guardrail. Not for finding what to target — use keyword-research or content-gap-analysis; not for drafting one standalone article — use content-writer; not for scoring a page for publish — use content-quality-auditor. 程序化SEO/批量页面/寄生SEO/借势权重/对比页/vs页/替代页/本地SEO/谷歌商家档案/NAP一致性

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Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.

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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.

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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

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Improve AI application with evaluation-driven development. Define eval criteria, instrument the application, build golden datasets, observe and evaluate application runs, analyze results, and produce a concrete action plan for improvements. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.

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Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.

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Build and verify Empty dataset state for Marketing landing with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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Build and verify Empty dataset state for Mobile-first interface with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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Build and verify Empty dataset state for Commerce and checkout with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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Build and verify Empty dataset state for Data-heavy application with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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Build and verify Empty dataset state for Documentation and knowledge with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design verification.

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