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Found 875 Skills
Use this skill when measuring CSAT, NPS, resolution time, deflection rates, or analyzing support trends. Triggers on CSAT, NPS, resolution time, deflection rate, support metrics, trend analysis, support reporting, and any task requiring customer support data analysis or reporting.
Extract market, financial, earnings, industry, and company metrics with Firecrawl. Use when the user asks for market research, industry trends, public company data, financial comparisons, earnings research, or structured market reports.
When the user wants to set up product analytics -- including event taxonomy, tracking plans, funnel analysis, or tool selection (Mixpanel, Amplitude, PostHog). Also use when the user says "event tracking," "analytics setup," "tracking plan," "analytics implementation," or "user identification." For PLG metrics, see plg-metrics. For experimentation, see growth-experimentation.
Alibaba Cloud MaxCompute Cost Analysis Skill. Analyze MaxCompute pay-as-you-go costs including billing, storage metrics, and compute metrics. Triggers: "maxcompute cost", "odps cost", "maxcompute billing", "maxcompute费用", "成本分析", "费用分析", "存储用量", "计算用量", "费用突增", "SQL签名", "SQL signature", "重复SQL", "扫描量最大", "daily billing details", "每日账单明细", "按计费项", "billing by fee item".
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.
Use when you need to implement or improve Java metrics observability with Micrometer — including meter design, naming/tag conventions, cardinality control, timers/counters/gauges/distribution summaries, percentiles/histograms, Actuator/Prometheus integration, and metrics validation through tests. This should trigger for requests such as Improve metrics; Apply Micrometer; Add metrics observability; Refactor Micrometer instrumentation. Part of cursor-rules-java project
Configures best-practice alerting policies for Google Cloud Vertex AI / Agent Platform agents on Agent Runtime. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, and quality metrics (response quality, tool use, hallucination). Also use when provisioning online monitors for quality evaluation, or analyzing live metrics traffic footprints. NOTE: This skill currently only works for the Agent Runtime. Don't use for configuring general GCP alert policies or non-agent GCP alerting policies.
Fetch Oura Ring sleep data using the ouraclaw CLI. Use when the user asks about their sleep score, sleep data, sleep stages, HRV, heart rate during sleep, bedtimes, or any Oura Ring data. Triggers on "sleep score", "how did I sleep", "oura data", "sleep data", "last night's sleep", "sleep quality", "HRV", or any request for Oura Ring metrics.
亚马逊反向选品：基于历史商业洞察报告沉淀的指标数据池，按 30+ 项商业维度（市场规模与增长、价格区间与档位份额、竞争密度与头部集中度、人群画像如年龄/性别/收入、评论卖点与痛点等）反向筛选亚马逊赛道与关键词。当用户提到反向选品、指标筛选、细分市场反查、蓝海赛道挖掘、低竞争赛道、新人友好赛道、品牌分散市场、痛点切入、卖点反查、定价档位机会、人群画像选品、Amazon niche reverse search, niche metrics filter, low-competition niche, blue ocean niche, demographic-based selection, pain-point niche, price tier opportunity, sweet spot pricing, brand fragmentation时触发此技能。即使用户未明确说"反向选品"，只要其需求是按商业维度筛选符合条件的亚马逊赛道，也应触发此技能。
Help users improve retention and engagement metrics. Use when someone is dealing with churn, optimizing activation flows, building habit-forming products, or trying to increase user engagement and lifetime value.
Set up comprehensive infrastructure monitoring with Prometheus, Grafana, and alerting systems for metrics, health checks, and performance tracking.
Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.