1,673 件 ・ 関連度順
概要と使いどころ
Estimates Fabric capacity cost before a migration by profiling Spark, SQL, Power BI, and Real-Time workloads, then recommending SKU size and comparing Reserved, Pay-As-You-Go, and Autoscale billing. Use only for capacity sizing and pricing questions, not for general migration or connectivity work.
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。
Manages Fabric Dataflow Gen2 items, including creation, M editing, connections, output destinations, getDefinition and updateDefinition inspection, executeQuery, refresh history, and Gen1-to-Gen2 save-as upgrades via saveAsNativeArtifact. Use for Dataflow items or Power Query M definitions. For pipeline JSON use pipeline-migration; for Spark or Warehouse transformations use their own skills.
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。
Manages Fabric IQ Ontology items, including entity and relationship types, data bindings, and definition updates, plus schema, lineage, grounding, and graph-walk exploration. Use for ontology modelling and traversal. For natural-language questions over a Power BI report use fabriciq.
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。
Governs Microsoft Fabric OneLake catalog health, protection, and trust through Fabric Admin, Core, and Power BI REST APIs. Use for tenant or owner-scoped audits and guarded remediation of domains, workspace assignment, capacity, labels, tags, descriptions, refresh, and item identity. Catalog item discovery belongs to search-consumption-cli.
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, crawling, and AI-powered answer extraction at scale
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 search, crawling, extraction, and deep research
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。
End-to-end champion/buyer/user job change signal composite. Takes a set of known people (past buyers, champions, power users), detects when they move to a new company, researches the new company for ICP fit, and drafts personalized outreach leveraging the existing relationship. Tool-agnostic — works with any people source, detection method, and outreach 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。
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。
Create a custom web dashboard (React + Vite + Express) inside your sandbox to visualize the agent's Turso database. The dashboard is served on port 3847 and the user sees it live in the "App" tab in Gooseworks. Use when the user asks for a dashboard, visualization, chart, metric view, or any custom UI powered by their agent's 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-agent-powered lead enrichment using Sixtyfour as primary source. Takes an email (+ optional name) and returns comprehensive person + company data with funding, AI/B2B classification, and full error visibility. Higher cost (~$0.20/lead) but simpler architecture.
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。
Detect buying signals across TAM companies and watchlist personas. Three-phase architecture: (1) free diff-based signals from existing data (headcount growth, tech stack changes, funding rounds), (2) Apify-powered signals (job postings, LinkedIn content analysis, profile changes), and (3) post-processing with dedup, scoring, and lead status updates. Writes signals to Supabase signals table for downstream activation.
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。
Create stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.
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。
Find speakers, hosts, and guest profiles at conferences and events on Luma. Two modes - free direct scrape for hosts, or Apify-powered search for full guest profiles with LinkedIn/Twitter/bio.
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 lead enrichment - find emails, phones, and enrich company/lead 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。
Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.
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。
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
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。
Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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 user mentions battery drain, energy optimization, power consumption audit, or pre-release energy check — timer abuse, polling patterns, continuous location, animation leaks, and background mode misuse.
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。
Add Apple Pencil drawing with PKCanvasView, PKToolPicker, PKDrawing serialization/export, stroke inspection, and PencilKit/PaperKit handoffs. Use when building drawing apps, annotation features, handwriting capture, signature fields, content-version-safe ink workflows, or Apple Pencil-powered experiences on iOS/iPadOS/visionOS.
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。
Query grid electricity forecasts and submit load events using EnergyKit to help users optimize home electricity usage. Use when building smart home apps, EV charger controls, HVAC scheduling, or energy management dashboards that guide users to use power during cleaner or cheaper grid periods.
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。
Create and edit presentation slide decks (`.pptx`) with PptxGenJS, bundled layout helpers, and render/validation utilities. Use when tasks involve building a new PowerPoint deck, recreating slides from screenshots/PDFs/reference decks, modifying slide content while preserving editable output, adding charts/diagrams/visuals, or diagnosing layout issues such as overflow, overlaps, and font substitution.
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。
在 Windows/macOS/Linux 上使用 `uv` 执行 Python 运行、依赖同步、锁文件管理、Python 版本管理与工具命令（`uv run`、`uv sync`、`uv lock`、`uv python`、`uv tool`）。当任务涉及“用 uv 运行脚本/命令”“临时依赖（`--with`）”“锁文件一致性（`--locked`/`--frozen`）”“跨平台 shell（bash/zsh/PowerShell）下排查 uv 执行问题”时使用此 skill。
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。
Smart triggered RF capture on NI USRP radios with Wireless Testbench — record only when a signal of interest appears, not continuously (triggered spectrum sensing). Use energy detection to capture when signal power rises above the noise floor, or preamble detection to cross-correlate against a known sequence (WLAN L-LTF, 5G NR PSS/SSS, LTE PSS, Zadoff-Chu, custom protocols) and capture only when that protocol's preamble is detected. Use when implementing triggered (wake-on-signal) capture or spectrum sensing, capturing protocol-based signals via cross-correlation, calibrating detection thresholds (plotDetectionSignals / plotThreshold), scanning frequency bands for activity, or building transmit-then-detect workflows.
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。