データ・AIのスキル

Use when the research design and method are the bottleneck for an Academy of Management Journal (AMJ) manuscript — matching design (archival, survey, experiment, multi-method, field) and level of analysis to the theoretical question. Designs the study; it does not run the estimation or validity checks (amj-data-analysis).

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 turning results into an explicit theoretical contribution for an Academy of Management Journal (AMJ) manuscript — the "what new theory do we learn?" statement and the discussion section. Frames the contribution and implications; it does not build the original theory (amj-theory-development) or run the analysis (amj-data-analysis).

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 running and reporting the statistical analysis for an Academy of Management Journal (AMJ) manuscript — measurement validity, common-method bias, the right estimator (HLM, SEM, panel, experiments), endogeneity, and robustness. Executes and reports the analysis; it does not design the study (amj-methods) or frame the contribution (amj-contribution-framing).

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 the theoretical argument and hypotheses are the bottleneck for an Academy of Management Journal (AMJ) manuscript — building a mechanism and deriving testable hypotheses a priori. Constructs the theory; it does not run the analysis (amj-data-analysis) or write the final contribution paragraph (amj-contribution-framing).

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 for full-manuscript prose polish of an Academy of Management Journal (AMJ) manuscript — front-loading the argument, active voice, structure, and AOM house style. Polishes language and structure; it does not create the theoretical contribution (amj-contribution-framing) or fix the analysis (amj-data-analysis).

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 packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement 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。

Orchestrate Project Osmos for long-running Fabric and OneLake data-engineering outcomes, including task status, messages, follow-ups, cancellation, and deletion. Osmos tasks are remote service tasks, not local background agents. In Fabric portal Copilot, direct interaction is unsupported; point users to GitHub Copilot CLI. Use spark-cli for direct non-Osmos Spark work. Triggers: "Project Osmos", "Project Osmos task status", "check my Osmos task".

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 Synapse workspaces to Fabric: Spark items, Lake Databases, Linked Services, and Dedicated SQL Pool schema/procedures for Lakehouse or Warehouse. During Pool-to-Lakehouse migration only, can migrate approved dependent procedure callers after notebook readiness. Excludes standalone pipelines and Warehouse administration. Triggers: Synapse workspace to Fabric, Dedicated SQL Pool to Lakehouse, Dedicated SQL Pool to Fabric Warehouse.

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 natural-language business questions over existing Power BI reports and semantic models through the FabricIQ MCP endpoint, discovering artifacts, resolving entities, generating the DAX itself, and returning a plain-language answer. Use when the user asks a data question, including one that says to query the model directly. For DAX the user supplies, or measures to save, use semantic-model-authoring.

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。

Migrates Synapse Data Factory pipelines to Fabric Data Factory, turning linked services into Fabric connections, inlining datasets into activities, converting global parameters to Variable Libraries, and mapping SynapseNotebook to TridentNotebook. Use for pipeline JSON. For Synapse Spark notebook code use synapse-migration.

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。

Check that a form actually rejects bad input: the error message renders, the submit button stays disabled, and no request goes out. Use when validation logic was written but never driven, when a "required" or pattern check looks right on screen but was never proven, or when a bug report says the form submitted invalid data anyway.

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。

Pipeline analysis composite. Pulls deal/meeting data from any CRM or tracking system, analyzes the pipeline over a user-defined period (weekly, fortnightly, monthly, quarterly), and produces both an executive summary and a detailed diagnostic report. Covers volume, qualification rates, source effectiveness, stage velocity, stuck deals, and actionable recommendations. Tool-agnostic — works with any CRM (Salesforce, HubSpot, Pipedrive, Close, Supabase, CSV).

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 customer feedback from multiple sources — support tickets, NPS comments, Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC report with theme clustering, sentiment analysis, trend detection, and actionable recommendations for product, marketing, and CS teams. Chains review-site-scraper for public review 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。

Search the web, platforms, and datasets. Use when asked to search, find, look up, research, or discover information from the web, YouTube, Amazon, eBay, news, academic sources, or any online platform.

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。

Perplexity AI search and chat completions with real-time web 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。

Scrape websites, extract structured data, and automate browsers. Use when asked to scrape, extract, crawl, parse, or pull data from web pages or any URL.

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。

Web scraping with structured data extraction - define your output schema

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。

Scrape G2, Capterra, and Trustpilot reviews for your product and competitors, then extract recurring themes, objections, proof points, and exact customer language for use in messaging. Chains review-site-scraper with LLM analysis. Produces a weekly or monthly digest that feeds directly into copywriting, positioning, and sales enablement. Use when a marketing team needs to ground messaging in real customer language.

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。

Prediction markets data - Polymarket, Kalshi markets, prices, positions, and trades

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。

Extract structured data from web pages using AI

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。

Research LinkedIn profiles and write personalized messages for any LinkedIn message type — connection requests, InMails, DMs, message requests, post comments, and comment replies. Takes LinkedIn URLs as input, researches each person (profile data + recent posts via Apify), and generates messages tailored to each lead's background, interests, and recent activity. Exports tool-ready CSVs for Dripify, Expandi, Botdog, PhantomBuster, or generic format. No LinkedIn cookies or login required.

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。

Hyperlocal weather data - precipitation, temperature, wind, soil moisture and more

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。

Email campaign/sequence performance review composite. Pulls campaign data (sends, opens, replies, bounces), reads actual email copy and subject lines, analyzes reply content (objections, positive interest, questions), and produces a diagnostic report covering quantitative metrics, copy quality, lead quality, and actionable recommendations. Tool-agnostic — works with Smartlead (MCP), Instantly, Outreach, Lemlist, Apollo, or CSV 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。

AI-powered web scraping - extract data using natural language prompts

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。