「metrics」の検索結果

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

日本語の概要は準備中です。原文の説明を表示しています。

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

日本語の概要は準備中です。原文の説明を表示しています。

Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.

日本語の概要は準備中です。原文の説明を表示しています。

Auto-instrument Node.js applications with distributed tracing, metrics, and logs.

日本語の概要は準備中です。原文の説明を表示しています。

Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.

日本語の概要は準備中です。原文の説明を表示しています。

Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.

日本語の概要は準備中です。原文の説明を表示しています。

Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.

日本語の概要は準備中です。原文の説明を表示しています。

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

日本語の概要は準備中です。原文の説明を表示しています。

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

日本語の概要は準備中です。原文の説明を表示しています。

Securing API Gateway endpoints with AWS WAF by configuring managed rule groups for OWASP Top 10 protection, creating custom rate limiting rules, implementing bot control, setting up IP reputation filtering, and monitoring WAF metrics for security effectiveness.

日本語の概要は準備中です。原文の説明を表示しています。

Financial factsheet analysis with key metrics extraction and investment rationale

日本語の概要は準備中です。原文の説明を表示しています。

Social media command desk (Busabase App-in-Skill) for Kelly's Twitter/X, Facebook, and Instagram accounts (extensible to LinkedIn, YouTube, Threads, TikTok, Xiaohongshu), covering cross-platform monitoring and ECHO-based publishing. Use when the user invokes $kelly-social or /kelly-social, wants cross-platform account metrics and timelines, or needs a content calendar, draft review, engagement inbox, crisis playbook, share-of-voice, or pre-publish quality gate.

日本語の概要は準備中です。原文の説明を表示しています。

Execute a single deterministic Python script to compute all derived financial metrics (EBITA, Invested Capital, Tax Rates, Summary Table) from extracted financial data.

日本語の概要は準備中です。原文の説明を表示しています。

Deploy DefectDojo as a centralized vulnerability management dashboard with scanner integrations, deduplication, metrics tracking, and Jira ticketing workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Implement a vulnerability aging dashboard and SLA tracking system to measure remediation performance against severity-based timelines and drive accountability.

日本語の概要は準備中です。原文の説明を表示しています。

Builds real-time incident response dashboards in Splunk, Elastic, or Grafana to provide SOC analysts and leadership with situational awareness during active incidents, tracking affected systems, containment status, IOC spread, and response timeline. Use when IR teams need unified visibility during incident coordination and post-incident reporting.

日本語の概要は準備中です。原文の説明を表示しています。

Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

日本語の概要は準備中です。原文の説明を表示しています。

Full SEO audit: Google Search Console data + URL Inspection API + PageSpeed Insights API + technical crawl + keyword research + metadata audit + schema markup audit + search intent analysis + Core Web Vitals monitoring. Feeds real GSC data and PageSpeed metrics into AI to surface quick wins, diagnose traffic drops, find content gaps, identify metadata mismatches, detect schema gaps, monitor page performance, and produce an actionable 30-day plan. Use this skill whenever the user asks about SEO, search rankings, organic traffic, Google Search Console, keyword performance, traffic drops, content gaps, search visibility, technical SEO, meta tags, schema markup, structured data, URL indexing, keyword research, indexing issues, page speed, performance, Core Web Vitals, LCP, INP, CLS, or Lighthouse scores. Also trigger on: "why is my traffic down", "what keywords am I ranking for", "improve my rankings", "check my search console", "SEO audit", "analyze my SEO", "technical SEO", "meta tags", "indexing issues", "crawl errors", "content strategy", "keyword cannibalization", "search intent", "schema markup", "structured data", "URL inspection", "page speed", "performance score", "core web vitals", "lighthouse", or any organic search question. If in doubt, trigger. This skill handles everything from quick GSC checks to deep technical audits with performance monitoring.

日本語の概要は準備中です。原文の説明を表示しています。

Guides experiment state transitions: launching, pausing, resuming, ending, shipping variants, archiving, resetting, duplicating, and copying to another project. Covers preconditions, implications for variant assignment and analysis, and the decision framework for when to use each action. TRIGGER when: user asks to launch, pause, resume, end, ship, archive, reset, duplicate, or copy an experiment to another project. DO NOT TRIGGER when: user is creating an experiment (use creating-experiments), configuring rollout (use configuring-experiment-rollout), or setting up metrics (use configuring-experiment-analytics).

日本語の概要は準備中です。原文の説明を表示しています。

Investigates a single PostHog error tracking issue end-to-end. Use when the user provides an issue ID or pastes an issue URL (`/error_tracking/<id>`) and wants to understand the error — who it affects, what triggers it, when it started, whether it correlates with a release, browser, OS, or feature flag, and what the next step should be. Pulls aggregated metrics, sample exception events, segment breakdowns, linked replays, and synthesizes a hypothesis-grade summary in one pass.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnoses bias, anomalies, and strange-looking results on a specific PostHog experiment. Covers empty / 0-exposure experiments, sample ratio mismatch, identity fragmentation, multi-variant exposure, uneven-split exclusion bias, significance traps (peeking, A/A, Bayesian vs Frequentist), PostHog-vs-SQL discrepancies, and surprises after mid-run edits. Symptom-driven dispatch to the right diagnostic. TRIGGER when: user asks 'is my experiment biased?' or 'why 0 exposures?', references the bias banner, says a variant looks strange / wrong / off, sees significance flipping, notices PostHog numbers disagreeing with their SQL, sees an A/A test showing significance, or reports surprises after mid-run edits. DO NOT TRIGGER when: creating a new experiment (use creating-experiments), only configuring rollout (use configuring-experiment-rollout) or metrics (use configuring-experiment-analytics), or only asking lifecycle questions (use managing-experiment-lifecycle).

日本語の概要は準備中です。原文の説明を表示しています。

Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.

日本語の概要は準備中です。原文の説明を表示しています。