1.5만건 · 관련도순
Guides systematic mapping of international personal data flows across an organisation. Covers system-by-system inventory methodology, third-party identification, transfer mechanism assignment, gap analysis, and data flow visualisation. Keywords: data flow mapping, international transfers, data inventory, transfer register, cross-border data flows.
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Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.
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Expert guide for Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocation_id tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.
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Manipulate row selection (`df.selection`) on a Datagrok DataFrame via the datagrok_exec tool — set, clear, invert, add to, remove from, intersect, and read the selection mask. Also covers current row (`df.currentRowIdx`), the cross-skill bridges to/from the filter, and how to materialize the selected rows as a new DataFrame. Use whenever the user says "select rows", "deselect", "invert selection", "highlight rows", "selected rows", "clear selection", "current row", "currentRowIdx", "count selected", "list selected indexes", "selection from filter", "filter from selection", or asks for selected rows as a new table. Does NOT cover row filtering (separate skill `datagrok-filtering`) or generic DataFrame cloning (`datagrok-df-and-columns`).
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architecture で選定したデータ基盤（Microsoft Fabric / Azure Databricks / Foundry IQ）を、事前確認 → plan と hash 承認 → デプロイ → セマンティック層 → runtime 検証 → 計算停止まで API で構築し、MCP 接続情報を Cowork / Copilot Studio / Foundry へ引き渡す。Dataverse は dataverse スキル、データ投入は data-migration スキルへ委譲する。
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CSV / JSON などの既存データを Dataverse / Azure Databricks / Microsoft Fabric Lakehouse へ移行する。ソースのプロファイル → マッピング契約の生成とレビュー → 検証と hash 承認 → 冪等な投入（upsert / MERGE / 全件ロード）→ 件数・キー・列値の照合までをスクリプトで行う。投入先の作成は dataverse / data-platform スキルが担当する。
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The branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.
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Guides users through discovering their database requirements, recommends a Google Cloud database based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', or when a user wants to create a new database on Google Cloud. Don't use for general Google Cloud maintenance, managing existing databases, or database migrations.
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Use when planning, reviewing, or troubleshooting Salesforce data imports, migrations, and bulk updates. Triggers: 'Data Loader', 'Data Import Wizard', 'Bulk API', 'upsert', 'cutover', 'load failed', 'reconcile data'. NOT for sequencing a whole multi-object migration with automation bypass and rollback — use data/data-migration-planning. NOT for choosing the upsert key itself — use data/external-id-strategy. Also triggers on 'process-conf.xml', 'sdl mapping file', 'sfdc.loadBatchSize', 'insert null values', 'insertNulls', '#N/A', 'serial mode', 'hard delete', 'assignmentRuleId', 'lineEnding', 'unprocessedrecords', 'failedResults', 'load order'.
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Create, design, and maintain Calculated Insights in Data Cloud: SQL authoring, dimension and measure definition, scheduling, streaming insights. Trigger keywords: calculated insight, CI SQL, insight measure, insight dimension, streaming insight, insight schedule, Data Cloud metric, insight refresh. NOT for segment filters, audiences and activation - use admin/data-cloud-segmentation. NOT for ingestion, DLO to DMO mapping and identity resolution - use data/data-cloud-data-streams.
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Use when a BA or admin needs to document the Salesforce data model: creating field inventories, object relationship maps, ER diagrams, or analyzing field usage across objects. Triggers: 'data dictionary', 'document our data model', 'object relationship map', 'field inventory', 'ER diagram for Salesforce'. NOT for designing the model or choosing lookup vs master-detail — use data/data-model-design-patterns. NOT for generating the Mermaid or PlantUML diagram — use architect/salesforce-erd-and-diagramming. More trigger keywords: field inventory CSV, describe API, getGlobalDescribe, DescribeFieldResult, sObject describe, schema snapshot, schema drift, blank field descriptions, undocumented fields, field ownership, data dictionary review cadence.
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Use when configuring or operating the native Salesforce Data Export Service (Setup → Data Export) — weekly or monthly CSV export, attachment inclusion, file-size split, the 48-hour download window, and the gap between this free utility and the paid Salesforce Backup and Restore product. Triggers: 'data export service', 'weekly export', 'export attachments', 'data export 48 hour download', 'data export missing objects', 'is weekly export a backup', 'bulk api 2.0 query job export', 'data loader extract batch', 'process-conf.xml export', 'incremental export systemmodstamp', 'export inventory retention owner'. NOT for the paid Salesforce Backup and Restore product (use that as a separate tool), NOT for Bulk API extraction (use data/bulk-api-patterns), NOT for HA/DR architecture (use architect/ha-dr-architecture).
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Use this skill when designing or building CRM Analytics Data Prep recipes — including node selection, join patterns, bucket field configuration, formula expressions, and scheduling. Triggers: 'build a recipe', 'join datasets in analytics', 'bucket a measure field', 'schedule a recipe', 'data prep transformation'. NOT for dataflow JSON and its node types — use admin/analytics-dataflow-development. NOT for tuning a slow or oversized dataset — use data/analytics-dataset-optimization.
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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).
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Design and safely evolve data models, database schemas, migrations, queries, indexes, and ORM data-access boundaries. Use for data-model, migration, query, index, ORM schema, or datastore-selection tasks. Discover the existing engine and migration source before proposing syntax.
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Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.
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Turns reported data problems (messages, tickets, meeting notes, complaints) into one DRAFT issue log: one row per issue with the symptom as reported, dataset and fields named, reproduction steps, affected reports, first seen date, reporters, a suggested owner, and blank severity, root cause and status cells for the owner. Never asserts a root cause, severity or fix. Use when the user asks to "log these data issues", "turn these complaints about the numbers into an issue list", "build the data quality backlog", "which reports are hit by this data problem" or "write up the reproduction steps for this data bug". Do not use for exploring or profiling a dataset, use dataset-insight-pack instead; for an IT incident write-up, use incident-postmortem-drafter; for tracking fix actions once assigned, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.
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Compare your data to a partner's data in the marketplace. Given a dataset you already own with person/edge data, this skill walks you through picking a partner data source to match against, choosing which identifier types to match on, optionally selecting which enrichment attributes to attach, and then submits the report — returning overlap, match counts, and demographic coverage. Use when: "how does my data compare to your marketplace", "compare my data to [partner]", "how much overlap do I have with [supplier]", "run a match report", "match my customers against 3P data", "see what enrichment is available for my dataset", or any open-ended question about marketplace overlap. (narrative-identity)
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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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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.
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Create a Databricks materialization to stream Estuary collections into Databricks tables via Unity Catalog. Use when setting up Databricks as a destination for captured data. Use when user says "send to Databricks", "materialize to Databricks", "Databricks destination", or "load into Databricks".
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Plan and execute database migrations, data transformations, and system migrations safely with rollback strategies and data integrity validation. Use when migrating databases, transforming data schemas, moving between database systems, implementing versioned migrations, handling data transformations, ensuring data integrity, or planning zero-downtime migrations.
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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).
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Excel과 CSV를 만들고 편집하며 흐트러진 표 데이터를 정리하고 형식을 변환합니다. 수식 재계산과 오류 확인, 서식 조정까지 거쳐 스프레드시트 파일로 완성합니다.