Data & Analytics Skills

Design a MotherDuck-backed customer-facing analytics app. Use for embedded analytics, multi-tenant SaaS reporting, or product analytics for external users -- whenever the decision depends on per-customer isolation, backend routing, service-account boundaries, read scaling, or Hypertenancy-style patterns.

Create, edit, manage, share, or embed MotherDuck Dives — live React + SQL dashboards, charts, and data apps saved in the workspace. Use for any dashboard, chart, KPI display, or data visualization over MotherDuck data, and for Dive authoring mechanics such as get_dive_guide, useSQLQuery, local preview, version history, Dives-as-code, required resources, team sharing, or embedded Dive sessions.

Build a live MotherDuck dashboard as a Dive. Use when composing one shareable KPI, trend, and breakdown story over existing MotherDuck data, especially when the result should stay a saved workspace artifact rather than a full application.

Design or redesign a MotherDuck Dive as a responsive, reusable analytics interface. Use when a Dive must be mobile-friendly from the start, support light and dark modes, reserve space for filters, use restrained Power BI-style information design, embed small charts inside metric components, or work across customers without one-off layout changes.

Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.

Design a MotherDuck-backed customer-facing analytics app. Use for embedded analytics, multi-tenant SaaS reporting, or product analytics for external users -- whenever the decision depends on per-customer isolation, backend routing, service-account boundaries, read scaling, or Hypertenancy-style patterns.

Build a live MotherDuck dashboard as a Dive. Use when composing one shareable KPI, trend, and breakdown story over existing MotherDuck data, especially when the result should stay a saved workspace artifact rather than a full application.

Design an end-to-end MotherDuck data pipeline. Use for ETL/ELT workflows -- choosing raw, staging, and analytics boundaries, bulk ingestion paths, transformation sequencing, dlt/dbt integration, publication targets, or whether DuckLake is actually required.

Create, edit, manage, share, or embed MotherDuck Dives — live React + SQL dashboards, charts, and data apps saved in the workspace. Use for any dashboard, chart, KPI display, or data visualization over MotherDuck data, and for Dive authoring mechanics such as get_dive_guide, useSQLQuery, local preview, version history, Dives-as-code, required resources, team sharing, or embedded Dive sessions.

Design or redesign a MotherDuck Dive as a responsive, reusable analytics interface. Use when a Dive must be mobile-friendly from the start, support light and dark modes, reserve space for filters, use restrained Power BI-style information design, embed small charts inside metric components, or work across customers without one-off layout changes.

Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.

> Create, list, download, update, and delete CrowdStrike Falcon Next-Gen SIEM lookup files. Upload CSV or JSON files for use with the match() function in CrowdStrike Query Language (CQL) queries. Use this skill when asked to manage lookup files, upload CSV data to CrowdStrike, create reference tables for SIEM queries, or work with Falcon Next-Gen SIEM lookup file operations.

AI Native 产品方法论——AI Native SaaS 行业案例模板 Skill。 用户提供 SaaS 产品场景和业务背景，Skill 自动执行全链路方法论： 方向定界 → 试验展开 → 系统构建 → 审计放行 → 生产运行，输出 AI Native 化改造方案。 基于《AI Native 产品方法论》第21章 AI Native SaaS 案例，适用于数据分析、业务洞察、决策支持场景。 '

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Workflow for analyzing bug reports, tracing root causes, and generating structured bug-fix implementation plans with rollback strategies.

Workflow to generate a comprehensive Product Requirements Document (PRD) detailing user stories, acceptance criteria, technical considerations, and metrics.

Build a Community Pack: strategy, platform plan, ambassador program, governance, metrics.

Produce a Cross-Functional Collaboration Pack (charter, stakeholder map, roles contract, decision log).

Stand up a Design Engineering practice: charter, prototype-to-production workflow, component delivery plan.

Create a Platform Strategy Pack (charter, interface map, ecosystem model, governance, metrics).

Define a product problem: problem statement, JTBD, alternatives, evidence, metrics. See also: writing-prds (solution spec).

Define or refresh a product North Star metric + driver tree.

> 🤖 Data Science & AI/ML skill suite derived from borghei/Claude-Skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.

> 🤖 Data Science & AI/ML skill suite derived from travisvn/awesome-claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.

> 🛒 E-commerce & Retail skill suite derived from ComposioHQ/awesome-claude-skills. Product catalogue optimisation, conversion rate, customer journey and retail analytics. Provides 10 specialised commands for ecommerce, retail, shopify workflows.

Generates periodic executive briefings (weekly/monthly/quarterly) by aggregating health status, critical alerts, key decisions, and upcoming milestones from all C-Suite domain dashboards into a single narrative update.

Defines metrics, events, dashboards, alerts, and SLOs to monitor production systems. Use after Gate 2 or with release-manager to ensure production observability.

>- Build and visualize QA dashboards and reports with Allure Report, Grafana, and ReportPortal. Covers test execution visualization, stakeholder-facing quality reports, trend/flakiness panels, release-readiness gates, alerting, and CI integration for automated report generation. "Grafana," "ReportPortal," "test results visualization." (this skill builds the panels; qa-metrics decides what they should show).

>- Analyze escaped defects and test suite health through blameless postmortems. Covers bug pattern analysis, test suite health reviews, 5 Whys root cause analysis, process improvement cycles, and postmortem/retro meeting templates with action item tracking. "improvement cycle." dashboards — use qa-metrics. Not for reviewing existing test code quality — use ai-qa-review.

>- Author and maintain MANUAL and hybrid test cases and suites in TestRail, Xray (Jira), Zephyr Scale, and Qase. Covers test-case anatomy (title, preconditions, steps, expected results, test data), suite/section organization, bulk authoring from user stories and acceptance criteria, ambiguous-step linting, CSV/API import-export payloads per tool, requirement traceability and coverage gaps, review hygiene, and when a manual case should graduate to automation. Scale case," "Qase case," "import CSV into TestRail," "lint these steps," "traceability report," "should this be automated." WHAT-to-test selection — that is test-planning.

> 🛒 E-commerce & Retail skill suite derived from alirezarezvani/claude-skills. Product catalogue optimisation, conversion rate, customer journey and retail analytics. Provides 10 specialised commands for ecommerce, retail, shopify workflows.

> 🤖 Data Science & AI/ML skill suite derived from ComposioHQ/awesome-claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.

| Validates practitioner credentials and license status against the NPI registry. Cross-references specialties, credentials, and practice addresses against official records. Returns Verified / Partially Verified / Unverified / Flagged per practitioner with mismatch details and source URLs. licenses", "verify NPI numbers", "cross-check credentials against NPI", "compliance audit on providers", "are these practitioners still licensed", "validate my provider list". Accepts CSV, Google Sheet URL, or pasted data. Do NOT use for extracting providers from practice URLs — use healthcare-providers-extract instead. Do NOT use for filling data gaps — use healthcare-providers-enrich instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.

| web-data agents, scrapes into Delta tables, and produces an AI/BI dashboard and/or a deployed Databricks App — a table → dashboard → app workflow, for production data products or quick demos. Use whenever a request pairs live or scraped web data WITH a Databricks destination — e.g. "scrape Amazon/Walmart prices into a Delta table and build a dashboard", "load Zillow/Instagram/Maps/search results into Databricks and build a dashboard or app", "showcase Nimble + Databricks to a prospect". Prefer it over nimble-web-expert or competitor-intel when the data lands in Databricks. Do NOT use for one-off web fetches or CSV exports with no Databricks destination — use nimble-web-expert instead. Do NOT use for competitor or company research briefings — use competitor-intel or company-deep-dive instead. Do NOT use for generic Databricks work with no Nimble/web-data angle — use the official databricks-* skills instead.

| Fills gaps in existing healthcare practitioner lists — adds missing phone numbers, credentials, specialties, contact info, education, reviews, and regulatory data. to these doctors", "complete this practitioner database", "enrich CRM export", "fill gaps in my provider data", "supplement this healthcare list". Accepts CSV, Google Sheet URL, or pasted data. Searches for each provider's practice website, extracts missing fields, and enriches with reviews, clinical trials, and accreditation via WSAs. Do NOT use for extracting providers from practice URLs — use healthcare-providers-extract instead. Do NOT use for validating credentials — use healthcare-providers-verify instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.

| Discovers all businesses of a given type in any geography using Nimble a user's existing list (Google Sheet, CSV, inline) against fresh discovery, categorizing entries as matched, discovered-only, or reference-only. Vertical presets (Healthcare, SaaS, Restaurants, Legal, Auto/Home) auto-select WSA routing. "account universe", "how many X in Y", "TAM for", "discover all", "audit my list", "compare against", "what am I missing", "gap analysis", "verify my business list", "prospect list". Do NOT use for competitor monitoring — use competitor-intel instead. Do NOT use for company deep dives — use company-deep-dive instead. Do NOT use for neighborhood-level exploration with social enrichment — use local-places instead.

| Monitors press, social, developer communities, and competitor channels from any point around a product launch — tracking sentiment, flagging mischaracterizations, surfacing competitor responses, and recommending actions in real time. Nimble reaches sources that block standard agents — including paywalled press, Reddit, LinkedIn, JavaScript-heavy pages, and live community forums — while Claude triages every signal by urgency. Delivers a Response War Room dashboard with a live signal feed, mischaracterization tracker, competitor response panel, and sentiment velocity chart. Use when asked to "monitor this launch", "track the launch", "what's being said about the launch", "flag any mischaracterizations", "competitor response to the launch", "post-launch coverage", "check press coverage for the launch", or "what's the reaction to the announcement". Do NOT use for ongoing brand monitoring unrelated to a launch — use brand-mention-monitor instead. Do NOT use for ongoing competitor intelligence unrelated to a specific launch window — use competitor-intel instead.

> Compares the seller's own Mercado Livre listing against the available competing listings of the same catalog product — the buy-box competition — and says where they win, lose, and what to fix first. Use it when a seller asks why they don't win the buy-box, why they don't sell despite the same product, or how they rank inside a catalog, given a Mercado Livre or JoomPulse link or a listing or catalog product identifier. It scores each parameter — price, free shipping, Full, listing type, seller reputation, official store — as Melhor, Na média, Pior, or Criticamente pior, presents reviews and rating as context, and returns a verdict, a comparison table, prioritized actions, and a spreadsheet. Triggers include "why don't I win the buy-box" and the pt-BR "por que não ganho o buy-box". Mercado Livre (Brasil) only; sales and revenue are JoomPulse estimates, not real transactions. For a product and its competitors, use the single-product analysis skill; for one over time, the change-monitor skill.

Use this agent when you need to analyze code for performance issues, optimize algorithms, identify bottlenecks, or ensure scalability. This includes reviewing database queries, memory usage, caching strategies, and overall system performance. The agent should be invoked after implementing features or when performance concerns arise.\\n\\n<example>\\nContext: The user has just implemented a new feature that processes user data.\\nuser: \"I've implemented the user analytics feature. Can you check if it will scale?\"\\nassistant: \"I'll use the performance-oracle agent to analyze the scalability and performance characteristics of your implementation.\"\\n<commentary>\\nSince the user is concerned about scalability, use the Task tool to launch the performance-oracle agent to analyze the code for performance issues.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: The user is experiencing slow API responses.\\nuser: \"The API endpoint for fetching reports is taking over 2 seconds to respond\"\\nassistant: \"Let me invoke the...

Report a bug in the compound-engineering plugin

> 🛒 E-commerce & Retail skill suite derived from travisvn/awesome-claude-skills. Product catalogue optimisation, conversion rate, customer journey and retail analytics. Provides 10 specialised commands for ecommerce, retail, shopify workflows.