Data & Analytics

Build board-room style dashboards, KPIs, and trend summaries over any Odoo data. Use when the user wants a report, dashboard, KPI, trend analysis, or executive summary.

Deduplicate, enrich, and fix data quality — especially contacts, products, leads. Use for dedupe, merging duplicates, fixing formatting, enrichment, or data-quality audits.

Creates data visualizations, charts, and interactive dashboards. Invoke when user wants to create plots, graphs, or visual representations of data.

互联网数据分析技能集合 - 包含AB测试、归因分析、内容分析、漏斗分析、增长模型、LTV预测等专业技能

Performs exploratory data analysis, statistical analysis, and pattern discovery. Invoke when user wants to analyze data, find patterns, statistical testing, or get deep insights.

通用的 6 阶段数据分析助手：数据质量→探索性分析→假设生成→可视化→代码生成→综合报告。提供完整的方法论和模板！

Data & Analytics advisory panel featuring Andrew Ng (DeepLearning.AI), Cathy O'Neil (ORCA), Cassie Kozyrkov (Google), Demis Hassabis (DeepMind), Timnit Gebru (DAIR), and Nate Silver (FiveThirtyEight).

Drive Google Analytics (GA4), Google Tag Manager, Google Search Console, and BigQuery from chat — tracking plans, GA4 reports, key-event (conversion) setup, custom dimensions and metrics, GTM audits, GSC performance, and GA4 BigQuery export queries. Use when the user wants an analytics audit, a GA4 report, a tracking plan, conversion setup, GTM cleanup, search-performance data, or asks "how is the site performing?" or "are my conversions firing?".

日志分析助手 — 智能解析日志文件，识别异常模式，定位问题根因

Self-service diagnostics — query Hope Agent's local SQLite databases (logs / sessions / async jobs) directly via the `exec` tool to investigate problems, analyze usage, and locate root causes. Trigger on: user reports something broken / failing / slow / stuck / not responding ('X 不工作', 'X 报错', 'X 卡住', '为什么 X 失败', 'why did X fail', 'show me the logs', 'check what happened'); ad-hoc data analysis ('this week's token usage', '最近调用最多的工具', 'how many subagent runs failed', 'tool error rate', 'find ...

Required reading whenever any chart_* tool is available. Teaches the one-tool embedding contract (call chart_render → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no chartjunk, restrained palette, proper typography, single message per chart), and the canonical spec patterns for the 12 most-common chart types. Skipping this leads to 1990s-Excel char...

State a falsifiable hypothesis, then build a Markdown table comparing what you'd Expect to see if it's true vs. the Actual data (or what data is needed). Use for designing analysis plans, pinpointing where reality diverges from expectation, or step 3 of a strategic analysis.

Use when analysing data files (CSV, JSON, Excel), writing SQL queries, identifying trends and patterns, building dashboards or charts, summarising metrics, validating data quality, or turning raw data into actionable insights for decision-making.

Precise metric definitions for data products. Outcome metric trees, naming conventions, grain specification, and the "what does this number mean?" problem. Use when defining KPIs, writing metric specifications, resolving conflicting metric definitions, building a metrics catalog, or when someone asks "how should we measure success?" or "why don't these numbers match?"

Healthcare data domain context covering FHIR, HL7, OMOP CDM, real-world evidence, and clinical terminology systems. Use when working on clinical data pipelines, EHR integrations, claims data products, HIPAA-governed data, OMOP transformations, or when the conversation involves PHI, ICD-10, SNOMED, CPT, LOINC, or RxNorm. Skip this skill for non-healthcare data products.

Decision tree exploration for data product plans. Interview relentlessly about schema decisions, consumer contracts, quality SLAs, and delivery choices. Use when planning a data product, designing a schema, choosing a delivery method, or when someone asks "grill me on this data product" or "what am I missing in this design?"

Data presentation and storytelling for data product operators. Narrative structures, chart selection, headline formulas, and anti-patterns. Use when presenting data to stakeholders, building a data presentation, writing an executive summary of findings, telling a data story, or making a case with data. For stakeholder alignment process, see stakeholder-alignment. For metric definitions, see metrics-definition.

Systematic data quality evaluation covering completeness, accuracy, timeliness, consistency, and validity. Use when assessing data pipelines, reviewing data product quality, auditing data sources, defining quality SLAs, building data quality monitors, or when someone asks "is this data trustworthy?" or "how do we measure data quality?"

Score whether a data product idea is worth building before committing resources. Validation scorecard, experiment design, and go/kill decisions. Use when evaluating feasibility, making go/no-go decisions, validating demand, sizing bets, or when someone asks "is this worth building?" or "should we invest in this?"

First-principles reasoning for data product decisions. Frames problems as data products, not dashboards or pipelines. Use when evaluating data product strategy, making build-vs-buy decisions, scoping data product features, assessing product-market fit for data offerings, or when someone asks "should we build this data product?"

Discover what internal data consumers actually need. Adapted Mom Test and JTBD for data teams. Use when conducting user research, interviewing stakeholders, gathering consumer requirements, running discovery sessions, or when someone asks "what do they need?" or "how do I figure out what to build?"

Convert dashboard requests into decision specifications. The missing layer between "build me a dashboard" and "help me decide." Use when receiving dashboard requests, reviewing analytics backlogs, prioritizing data team work, or when someone asks "what dashboard do you need?" Apply this BEFORE building anything.

3 diagnostic questions for evaluating data product markets through the arbitrage gap lens. Identifies whether your data product sits on a durable or closing advantage. Use when assessing data product positioning, evaluating market risk, or when someone asks "is AI going to replace this?" or "what's our moat?"

Loads CSV, Excel, and JSON data files, performs statistical analysis, and generates charts and reports. Use when the user asks to analyze a dataset, compute statistics, create visualizations, find trends, or produce a data report.