Results for “analytics”

Expert guidance for Ibis, the Python dataframe library that provides a pandas-like API but generates SQL for execution on any backend — DuckDB, PostgreSQL, BigQuery, Snowflake, Spark, and more. Helps developers write analytics code once and run it anywhere without rewriting SQL for each database.

Provides Superwall REST API access, ClickHouse data analytics, documentation lookup, SDK integration triage, dashboard linking, and SDK source cloning. Use when the user asks about Superwall paywalls, campaigns, subscriptions, API usage, data analysis, SDK integration, webhook events, or debugging SDK behavior.`

Design and plan end-to-end Azure IoT and Smart City solutions: requirements, architecture, security, operations, cost, and a phased delivery plan with concrete implementation artifacts.

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs.

Runs a full marketing and sales operation for solo founders, covering customer research, content creation, SEO, paid ads, email sequences, CRO, pricing, retention, analytics, and automation with stage-based playbooks.

UTM parameter strategy, campaign tracking, and marketing attribution modeling. Build consistent UTM taxonomies, configure attribution models, measure cross-channel performance, and connect marketing spend to revenue. Use when the user asks about UTM parameters, campaign tracking, attribution models, marketing measurement, or cross-channel analytics.

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see data-and-funnel-analytics.

Use this skill when the user asks about trucking rate negotiation — cost-plus pricing, market rate benchmarking via DAT/Truckstop, fuel surcharge math, how to negotiate with brokers/shippers, when to walk away, contract pricing vs spot rates. Reference DAT Rate Analytics + freight market reports.

Instrument browser/web apps with the Application Insights JavaScript SDK for Real User Monitoring (RUM), including page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and GenAI agent traces correlated to backend OpenTelemetry traces.

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.

Guide for accessing financial and economic data in MATLAB using the Datafeed Toolbox. Covers Bloomberg (market data via bloomberg/blp/bloombergHypermedia), FRED (Federal Reserve economic data via fredrs), and Haver Analytics (economic data via haver/haverdirect/haverview). Use when connecting to any of these data providers from MATLAB.

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

Central router for the marketing skill ecosystem. Use when unsure which marketing skill to use, when orchestrating a multi-skill campaign, or when coordinating across content, SEO, CRO, channels, and analytics. Also use when the user mentions 'marketing help,' 'campaign plan,' 'what should I do next,' 'marketing priorities,' or 'coordinate marketing.'

Token budget analytics and ROI scoring for AI agent actions. Track token cost per fix, detect wasteful loops, auto-route to fast tier when burn rate is too high. Integrates with token-budget-guard.sh hook. Triggered by /cost-report, "how much did that cost", "token usage", "am I wasting tokens".

Marketing skills router: match marketing intent to a sub-skill, then Read its Entry file under skills/<name>/ (e.g. ads/ads.md). Covers CRO, copywriting, SEO/AEO, paid ads, ad creative, analytics, email/SMS, pricing, launches, video/image generation, and growth loops. Use for any marketing request — campaigns, content, optimization, measurement.

TikTok Shop e-commerce data assistant. Search products, find trending items, analyze influencers, explore shops, track video performance, and get ad insights via ecomseer.com. Triggers: 找商品, 搜商品, 爆品, 带货, TikTok电商, 达人分析, 视频带货, 店铺分析, 广告素材, 销量榜, 跨境电商, search products, find trending, TikTok Shop, influencer analysis, shop data, ad creatives, sales ranking, e-commerce analytics, product research.

Build predictive models for sports and event outcomes using statistical methods, ELO ratings, regression, Monte Carlo simulation, and machine learning. Use when creating power rankings, projecting game outcomes, estimating win probabilities, or building a quantitative edge. Also trigger for 'prediction model', 'ELO rating', 'power rankings', 'win probability', 'Monte Carlo', 'regression model', 'expected goals', or 'predictive analytics'.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls, and potential data exfiltration using statistical baselines.

Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.

Enterprise AI adoption platform for Claude Code — measures team usage via OpenTelemetry, syncs skills/MCP servers/hooks from a central dashboard, and delivers context-aware skill suggestions at prompt time. Solves the Intention-Action Gap: organizations that deploy Zeude see 3x adoption improvement (6% → 18%). Requires Supabase (config) and ClickHouse (analytics). Triggers on: zeude, ai adoption, claude code adoption, enterprise claude, opentelemetry claude, skill sync, team claude management.

Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Expert knowledge for Azure Sre Agent development including troubleshooting, best practices, decision making, security, configuration, integrations & coding patterns, and deployment. Use when wiring SRE Agent to Azure DevOps/GitHub, Log Analytics/App Insights, AKS Java workloads, or Key Vault, and other Azure Sre Agent related development tasks. Not for Azure Monitor (use azure-monitor), Azure Reliability (use azure-reliability), Azure Resiliency (use azure-resiliency), Azure Service Health (use azure-service-health).

Expert knowledge for Azure DevOps development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing org/projects, Boards/work items, repos/pipelines, Analytics/OData/Power BI, or Azure DevOps Server, and other Azure DevOps related development tasks. Not for Azure Boards (use azure-boards), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

This skill should be used when the user asks about Hyperliquid whale positions, Hyperliquid liquidations, Hyperliquid open interest, Hyperliquid trader analytics, Hyperliquid taker data, smart money on Hyperliquid, or any Hyperliquid-specific query. Use when user says: 'Hyperliquid whales', 'HL whale positions', 'HL liquidations', 'HL open interest', 'HL trader', 'smart money', 'Hyperliquid大户', 'HL鲸鱼', 'HL持仓', 'HL清算', 'HL持仓量', 'HL交易员'. For general crypto prices/news, use aicoin-market. For exchange trading, use aicoin-trading. For Freqtrade, use aicoin-freqtrade.

Expert knowledge for Azure Sentinel development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Sentinel data connectors, analytics rules, playbooks/Logic Apps, ASIM schemas, or SAP/AWS/GCP sources, and other Azure Sentinel related development tasks. Not for Azure Defender For Cloud (use azure-defender-for-cloud), Azure Security (use azure-security), Azure Monitor (use azure-monitor), Azure Network Watcher (use azure-network-watcher).

Expert knowledge for Azure Monitor development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Log Analytics workspaces, AMA/DCRs, Application Insights, Prometheus/AKS, or Grafana integrations, and other Azure Monitor related development tasks. Not for Azure Managed Grafana (use azure-managed-grafana), Azure Network Watcher (use azure-network-watcher), Azure Service Health (use azure-service-health), Azure Defender For Cloud (use azure-defender-for-cloud).

Amazon Ads deep analysis covering Sponsored Products, Sponsored Brands (incl. Sponsored Brands Video), Sponsored Display (audiences + contextual), and basic Amazon DSP. Evaluates campaign structure, ACOS/TACOS targets, search-term harvesting, negative keyword discipline, Brand Analytics signals, day-parting, bid management, auto vs manual campaign mix, ASIN targeting, and DSP retargeting. Use when user says Amazon Ads, Amazon advertising, Amazon PPC, Amazon search ads, Sponsored Products, Sponsored Brands, Sponsored Display, Amazon DSP, ACOS, TACOS, retail media audit, Amazon Marketing Services, AMS, or Amazon seller advertising.

Integrate with SAP systems and build extensions. Use when a user asks to connect to SAP S/4HANA, SAP Business One, or SAP ERP via OData, RFC, BAPI, or IDoc interfaces, build SAP BTP (Business Technology Platform) applications, work with SAP CAP (Cloud Application Programming), consume SAP APIs from the API Business Hub, manage master data, automate procurement or sales processes, build Fiori apps, extract SAP data for analytics, or integrate SAP with external systems. Covers S/4HANA APIs, Business One, BTP, CAP, and integration patterns.

Turn account data into a QBR that earns the renewal. It builds the value-delivered story, adoption against the goals they signed up for, the open risks stated plainly, the forward roadmap, and the expansion ask, then hands you a deck outline. Built for B2B customer success teams, customizable to your CRM and product analytics. Trigger on "build a QBR", "prep the business review", "quarterly review for this account", "what do I show the customer", "value story for the renewal", or any account-review prep.

Turn an at-risk account into a churn-save play. It diagnoses the real reason behind the risk (not the stated one), lays out the commercial and non-commercial levers you can pull, and builds a multi-touch save sequence with owners and dates. Built for B2B customer success teams, customizable to your CRM and product analytics. Trigger on "this account is going to churn", "build a save play", "why are they leaving", "at-risk renewal", "how do I keep this account", or any retention rescue.

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.

Turn a customer's stated goals into a success plan that predicts the renewal. It sets time-to-value milestones, names an owner on both sides for each, and picks the leading indicators that tell you a year early whether this account renews. Built for B2B customer success teams, customizable to your CRM and product analytics. Trigger on "build a success plan", "onboarding plan for this account", "what are the milestones", "how do I know they'll renew", "time-to-value plan", or any post-sale planning.

Turn "is this new customer going to stick?" into a first-90-day diagnostic. Day-30 / 60 / 90 activation tracking, a sales-to-success handoff quality score, champion engagement during the onboarding window, time-to-value milestone hit rate, and an early false-negative catch on customers already adopting. Built for CS leaders and CSMs, customizable to your CRM and your product-analytics tool. Trigger on "how's {account} onboarding going?", "first 90 day check on {customer}", "is {account} activating?", "time-to-value on {customer}", "handoff quality", "who's at risk in first 90 days?", or any first-90-day adoption question.