673 件 ・ 関連度順
概要と使いどころ
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical 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。
Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.
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 publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.
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。
Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.
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 defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
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。
Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code quality, refactoring priority, debt scoring, cleanup sprints, or code health assessment. Also use for legacy code modernization planning and maintenance cost estimation.
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。
Re-evaluate open bugs — priority vs severity, assign to sprints, surface systemic trends. Run when the count grows.
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。
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
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。
Weekly QBR-prep runbook. Finds every account whose next QBR falls within {{lookahead_days}} days of HubSpot's {{qbr_date_property}} field, pulls usage trends from Postgres and account/CSM data from HubSpot, and assembles a draft deck plus a briefing doc under {{decks_folder}} — read-only across every source system.
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。
Improve LifeOS from what the best practitioners are shipping around AI harnesses — Anthropic first (changelogs, docs, releases), then trusted creators, trending repos, and the system's own reflections — extracting concrete techniques and filtering them against verified current state so nothing already-done or rejected is re-recommended. USE WHEN upgrade, system upgrade, check Anthropic, new Claude features, algorithm upgrade, LifeOS upgrade, mine reflections.
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。
Multi-agent web research with mandatory URL verification, confidence-tagged output, and four depth modes (quick to deep investigation). USE WHEN research, do research, quick research, extensive research, deep investigation, find information, investigate, extract alpha, analyze content, retrieve content, AI trends, enhance content, extract knowledge, web scraping, YouTube extraction, map landscape, competitive analysis, find it, find this, find this product, identify this, what is this, what's that thing, track down, locate, help me find, I can't find X online, can't find it online, source this — never substitute raw WebSearch/WebFetch for a multi-source find/identify/investigate request. NOT FOR people/company/entity deep background, academic papers (use ArXiv), JSON entity extraction, or content-adaptive wisdom extraction (use ExtractWisdom).
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。
Live SEO data via DataForSEO MCP server: SERP analysis, keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis, competitor and content analysis, business listings, AI visibility (LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "AI visibility check", or "real search 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。
Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", or "real CWV 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。
Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.
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 and progressively read open-access academic papers through DeepXiv. Use when the user wants layered paper access, section-level reading, trending papers, or DeepXiv-backed literature retrieval.
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。
Resolve "last30Days" to a concrete ISO date range relative to your run time — a rolling 30-day window ending today. Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a "last 30 days" / trailing-month task (monthly trends, recent activity).
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。
电商运营在开展跨境电商或闲鱼捡漏业务时，若需解决选品难、上架繁琐等痛点，必用此技能！一键打通“爆品挖掘→1688采集→多平台上架→推广文案→短视频生成”全自动流水线，轻松实现端到端自动化，让开店运营效率翻倍。
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。
Verify a production fix by linking the changed code and release to Sentry issue state and post-release event trends without resolving issues prematurely.
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。
Polymarket screeners — discover trending events, top markets by volume, and search for specific markets. Use when browsing what's happening on prediction markets.
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。
Access real-time crypto token data, DeFi analytics, wallet intelligence, project research, and blockchain insights through Heurist Mesh agents. Use when the user asks about token trends, DeFi protocols, wallet holdings, project due diligence, Twitter/X crypto sentiment, or deeper market 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。
This skill helps users extract structured data from Reddit posts and comments via BrowserAct API. Agent should proactively apply this skill when users express needs like analyzing competitor mentions on Reddit, tracking brand sentiment in Reddit comments, extracting Reddit discussions for market research, finding popular Reddit posts by keywords, monitoring community feedback on specific topics, gathering user reviews from Reddit threads, searching for Reddit posts within a specific date range, sorting Reddit discussions by relevance or hotness, compiling nested Reddit comments for deep analysis, building a structured dataset of Reddit conversations, discovering trending topics in specific subreddits, or monitoring social media activity for specific brands on Reddit.
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。
Searches Threads posts by keyword or hashtag and returns matching posts with engagement metrics, extracted from SSR-embedded JSON. Use when user asks to search Threads posts, find Threads content by topic, scrape Threads search results, collect Threads posts about a keyword, monitor Threads hashtag activity, pull Threads posts mentioning a term, gather Threads content by hashtag, search for posts on Threads, extract Threads search feed, get trending posts on Threads, find Threads discussions about a subject, or fetch recent or top Threads posts by keyword.
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。
This skill helps users extract full article contents from WeChat using the BrowserAct API. The Agent should proactively apply this skill when users express needs like finding full WeChat articles for specific keywords, tracking WeChat public accounts for industry trends, extracting WeChat article contents for media research, monitoring public relations on WeChat platforms, collecting competitor updates from WeChat, getting full article body from WeChat links, monitoring brand exposure on WeChat articles, retrieving structured WeChat data for sentiment analysis, summarizing daily news from WeChat, getting author and publication date for WeChat articles, or automating WeChat content extraction without scraping.
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。
This skill helps users automatically extract detailed video metrics and channel information from YouTube based on keyword searches using the BrowserAct API. The Agent should proactively apply this skill when users express needs such as extract specific keyword YouTube video detailed data, monitor the latest video performance of competitor channels, collect comment and like counts for videos on a specific topic, find AI agent tutorials published this week and extract metrics, evaluate total views and subscriber info for specific videos, scrape detailed metrics of marketing campaign videos, track video trends for a tech topic periodically, get high quality video list data for specified keywords on YouTube, mine detailed information of the latest YouTube videos, collect video duration and engagement data for specific industries, or monitor YouTube content creator performance metrics.
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。