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Found 802 Skills
Expert product launch strategist for SaaS and technology companies. Use when planning product launches, coordinating cross-functional launch teams, managing beta programs, creating launch communication plans, planning launch day execution, setting up post-launch monitoring, running launch retrospectives, or defining launch metrics. Covers launch tiering, internal enablement, rollback planning, and contingency strategies.
Convert an Omni Analytics topic into a Databricks Metric View definition in Unity Catalog. Use this skill whenever someone wants to export Omni metrics to Databricks, create a Metric View from an Omni topic, harden BI metrics into Unity Catalog, or bridge Omni's semantic layer with Databricks AI/BI dashboards and Genie spaces.
Create and customize Syncfusion Angular Linear Gauge components for displaying measurements and metrics. Use this skill when user needs to implement a linear gauge, display scalar values in a linear scale, create measurement interfaces, configure ranges and pointers, add annotations, handle user interactions, or customize gauge appearance. Covers installation, configuration, events, accessibility, printing, and internationalization.
How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.
Use when improving performance, latency, throughput, memory usage, or general efficiency. Start by defining target metrics, measuring comprehensively, attributing bottlenecks, validating with static analysis, and prioritizing macro-optimizations before micro-optimizations.
Explains business financial terms and frameworks for engineering managers — produces term definitions (ARR, COGS, CAC, LTV, gross margin, burn rate, EBITDA, AARRR), translation formulas for making engineering work visible in business language, and a three-layer framework for building business credibility. Use when the user says "business terms," "EBITDA," "burn rate," "CAC," "LTV," "gross margin," "ARR," "how do I speak to business people," "I don't understand finance," "make the case for engineering work," "connect engineering to business outcomes," "talk to the P&L owner," or "business impact." Do NOT use when the user wants to connect engineering metrics (DORA, velocity) to business metrics — use developer-productivity instead.
Leverage the market statistics capability of SellerSprite to output a market statistics dashboard by category node, including metrics such as average rating, average price, BSR, sales volume, number of sellers, and new product-related indicators for top Listings. It is suitable for quickly judging the market quality and competitive landscape of a certain category. This skill is triggered when the user mentions category market statistics, market selection dashboard, market foundation assessment, node market quality, top product statistics, SellerSprite market statistics, or category statistics. Even if the user does not explicitly mention "SellerSprite", this skill should be triggered as long as the requirement is to view aggregated statistical results by category node.
Cross-market financial metrics batch query — revenue, net profit, ROE, debt ratio, free cash flow, gross margin for one or more symbols across HK / US / A-share / SG markets. Supports multi-symbol horizontal comparison, similar to natural-language financial screening. Triggers: "财务数据查询", "财务指标", "营收查询", "净利润查询", "ROE查询", "负债率", "现金流查询", "毛利率查询", "财务数据批量", "財務數據查詢", "財務指標", "營收查詢", "淨利潤查詢", "ROE查詢", "負債率", "現金流查詢", "毛利率查詢", "financial data query", "revenue query", "net profit query", "ROE query", "debt ratio", "free cash flow", "gross margin query", "financial metrics", "financial comparison", "batch financials".
Install and configure the DefiLlama MCP server for DeFi analytics. Provides 23 tools for TVL, token prices, yields, protocol metrics, stablecoins, bridges, ETFs, hacks, raises, and more. Supports OAuth login with your DefiLlama account. Use when the user wants to set up DefiLlama MCP, connect to DeFi data, or install DeFi analytics tools.
Configures the analytics side of a PostHog experiment — exposure criteria (default `$feature_flag_called` vs custom exposure events), primary and secondary metrics, the supported metric types (count, sum, ratio with `math` and `math_property`, retention with `retention_window_start` and `start_handling`), multivariate user handling ("Exclude" vs "First seen variant"), and how to read results once the experiment is live. Use when the user adds or edits a primary or secondary metric (e.g. "add a secondary metric tracking 'downloaded_file' per user"), sets up a ratio metric (e.g. "revenue from purchase_completed / pageviews"), sets up a retention metric (e.g. "$pageview → uploaded_file, 7-day window"), configures custom exposure (e.g. "only count users who hit /checkout"), changes multivariate handling, or asks "who is in the analysis?", "how do I measure impact?", "is this winning?", "what's the confidence level?", or "should I ship?".
Query the Genome Aggregation Database (gnomAD). Use when determining the rarity or allele frequency of specific genetic variants, retrieving gene constraint metrics (pLI, LOEUF) to assess loss-of-function intolerance, finding variants in a genomic region or gene, or querying structural variants. Don't use for analyzing individual patient genomes, tracking somatic mutations in cancer (use COSMIC), or requesting raw sequencing reads (use ENA).
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.