'analytics' 검색 결과

Run analytics queries against any database using plain English — BigQuery (bq CLI), PostgreSQL, MySQL, SQLite, or any DB with a CLI/MCP/API. Use when you need to pull metrics, analyze data, or answer business questions without writing SQL.

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When the user wants to plan, design, or implement an A/B test or experiment — "split test," "variant copy," "multivariate test," "hypothesis," "statistical significance," "how long should I run this test," or comparing two versions. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-cro.

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Explains and designs Microsoft Fabric Lakehouse solutions: Delta tables, the SQL analytics endpoint, shortcuts, and AI functions. Use when building, optimizing, or documenting a Fabric Lakehouse.

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Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.

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Exasol database interaction via the exapump CLI and Exasol SQL. Covers SQL queries and DML/DDL such as `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `MERGE`, `CREATE TABLE`, `ALTER TABLE`, and `DROP TABLE`, `CREATE CONNECTION` connection objects without import or export intent, schema inspection, table design, `exapump sql` and `exapump profile`, query profiling, analytics, and Exasol-specific SQL behavior outside exasol-import and exasol-export.

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Make public, read heavy Convex + Vite pages load fast on first visit, return visit and in app navigation. Use when a public page feels slow, when adding a new public page, analytics, images, markdown or other heavy dependencies, when touching index.html, netlify.toml, _headers, _redirects or vite.config.ts, or when the user mentions "slow", "load time", "LCP", "bundle size", "waterfall", "websocket handshake", "cache headers", "thumbnails" or "far from the deployment".

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Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

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Hunt AADGraphActivityLogs and MicrosoftGraphActivityLogs in Microsoft Sentinel/Log Analytics for fingerprints of offensive Entra ID tools such as ROADtools, AADInternals, and AzureHound.

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This skill covers detecting cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion detection systems, industrial protocol anomaly detection, and process data analytics to identify attacks that traditional IT security tools miss.

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Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

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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 (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.

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Model Context Protocol server exposing 53 tools, 3 resource types, and 3 prompts for AI agent consumption — quoting, building transactions, fee management, analytics, AMM operations, social fees, wallet operations over stdio transport.

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Produces a DRAFT product requirements document from the discovery notes, interview summaries, support themes, analytics notes and stakeholder messages the user provides: problem statement, target users, goals with measures as stated, numbered requirements each with a testable acceptance criterion, non-goals, constraints, dependencies and open questions, with every statement tagged evidenced, assumed or UNKNOWN so each assumption is visible for the product owner to confirm. Use when the user asks to "write a PRD", "draft the product requirements", "turn these discovery notes into requirements", "document what this feature needs to do" or "build the requirements doc for this epic". Do not use for supplier-facing requirements in a tender, use rfp-requirements-pack instead; for a technical design decision, use architecture-decision-record; for the change ticket that ships the work, use change-request-pack. Drafts for human review; never approves, authorises or signs off.

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Review product designs before engineering handoff or release for flow and state coverage, business-rule alignment, implementation clarity, accessibility, responsiveness, analytics, and testability.

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Generate professional documents — PDF, Word (.docx), Excel (.xlsx), and invoices — from natural language descriptions or structured data. Covers AI-powered and simple document creation, HTML-to-PDF conversion, PDF extraction/OCR, merging, watermarking, and data export. Use when you need to create campaign decks, reports, proposals, invoices, spreadsheets, or any shareable document — especially for Step 7 monthly presentations, campaign briefs, and analytics reports.

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Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

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Dashboard metric register: metric, source module, formula, period, value, target, trend, owner and last-updated, as CSV, SQL, JSON Schema or Notion on request. Use for KPI dashboards.

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Use when the user asks to "build our social posting calendar", "set weekly slots and queue depth per channel", or "plan the evergreen recycle rotation"; produces the always-on brand calendar — pillar allocation with hero/hub/help balance and give:ask targets (all labeled Estimated starting heuristics to calibrate against own analytics, never scored rules), per-channel recurring slots with queue depth, a batching workflow, an evergreen recycle cycle with freshness re-checks, deliberate open slots for realtime/trend moments fed by trend-spotter go/skip verdicts, and first-comment/publish-order ops — cadence commitments filed to the channel registry, and no batch ships to the queue without a SHIP verdict from social-quality-auditor pre-publish mode. Not for repurposing an existing asset or the paid-amplification calendar — use content-amplifier. 社媒排期/内容日历/栏目配比/常青循环复用

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Use when the user asks to "measure how our narrative is landing", "track echo rate against our canon lexicon", or "check how AI answer engines describe our brand"; produces a resonance report — echo rate (overlap of market language with the narrative-registry canon lexicon, method declared), AI-answer perception via tavily.py --answer (proxy-labeled), share-of-voice on a locked competitor panel (reusing share-of-voice-tracker), and resonance signals from bluesky.py / gdelt.py / pageviews.py — every number labeled Measured / proxy / User-provided, feeding the TALE E dimension and the upstream of the E1 evidence-integrity veto. Not for rebuilding share-of-voice machinery — use share-of-voice-tracker; not for own-site GA4/GSC analytics — use performance-monitor; not for scoring TALE profile result — use narrative-quality-auditor; not for adjudicating claims — use offer-claims-registry. 回声率/AI回答感知/份额之声/共鸣信号

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Use when the user asks to "run a launch retro / post-mortem", "compare launch results vs targets by channel", or "decide what to keep or kill for the next launch"; produces a structured D1/W1/M1 retrospective — a per-channel actual-vs-target table (UTM-attributed own analytics as the truth column, platform self-reported numbers as reference, every figure labeled Measured / User-provided / Estimated), a 5-Whys chain on the single largest miss, keep / kill / change decisions per channel, 3-5 actionable learnings for the next launch, and an outcome snapshot submitted to the launch registry. Not for return math (CPA / ROI) — use roi-calculator; not for the stakeholder-facing report writeup — use report-generator; not for a metric deep-dive — use performance-analyzer. 发布复盘/渠道归因/5-Whys/keep-kill

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Use when the user asks to "package the launch assets", "build a press kit", or "prep the store listing and go-live checklist"; produces a tier-scoped launch asset manifest with production status — a press kit spec (factsheet, description, history, features, videos, images, logo and icon, awards, contact), demo script and screenshot specs, a launch FAQ, dual-store listing metadata drafts against the official character budgets (per App Store Connect / Play Console documentation), and a technical go-live checklist manifest (robots flip, sitemap, OG tags, analytics verification — execution stays with technical-seo-checker and serp-markup-builder). Not for the message copy itself — use message-house-builder or content-writer; not for landing page UX — use landing-optimizer; not for keyword research beyond the store surfaces — use keyword-research. 发布资产打包/press kit/商店listing规格/上线检查清单

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Use when the user asks to "figure out where our direct traffic really comes from", "measure dark social", "add a how-did-you-hear-about-us field", or "show social drives signups without click data"; produces a share-link/UTM hygiene spec for owned share surfaces, a self-reported attribution field design that replaces an existing form field (free-text first, coded later), a GA4 direct-traffic decomposition read (deep-URL directs, mobile-app skew, private-push correlation) with every derived number hard-labeled Estimated/proxy, and a branded-search-lift proxy from GSC plus Wikipedia pageviews — the declared dark-social method behind ECHO O2. Not for paid-channel attribution reconciliation (platform-claimed vs analytics conversions) — use attribution-reconciler. 暗社交归因/直接流量分解/自报来源字段/分享链路UTM

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Complete B2B SaaS marketing agency powered by 79 AI agents across 17 specialties. This is the entry point for ALL marketing requests. Routes to the right specialist team: content marketing, SEO, paid media, social media, email marketing, design, sales enablement (including technical presales — demos, proofs of concept, security questionnaires), product marketing, account-based marketing (ABM), growth, analytics, project management, or client operations. Use this skill when the request spans multiple disciplines or when unsure which specialist to invoke. Also triggers on: marketing help, marketing team, marketing agency, what can you do, marketing capabilities.

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Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

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