「amplitude」の検索結果

Amplitude Automation via Rube MCP workflow skill. Use this skill when the user needs Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Amplitude Automation via Rube MCP workflow skill. Use this skill when the user needs Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Amplitude integration. Manage Users, Events. Use when the user wants to interact with Amplitude 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。

Use when working with Amplitude — amplitude analytics management — monitor event ingestion, user activity, chart dashboards, cohorts, and data taxonomy. Use when reviewing event volumes, inspecting user funnels, auditing taxonomy health, or debugging ingestion issues.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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 Specialist IA — Expert en analytics (GA4, Mixpanel, Amplitude, event tracking, dashboards).

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Define time-varying amplitudes. Use when user mentions ramp, time-varying, cyclic, pulse, or gradually increasing loads. Does NOT handle static constant loads.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Estimate heart rate (HR) and breathing rate (BR) in bpm from a cleaned 1-D physiological time series such as radar phase, WiFi CSI amplitude, PPG, or a chest-motion signal. Use when Claude needs to choose HR/BR bandpass edges, pick a peak frequency from a noisy band, reject the HR second harmonic that often dominates the fundamental, handle respiration-harmonic leakage into the HR band on slow breathers, or flag low-confidence estimates. Not for arrhythmia / beat-to-beat analysis, multi-subject source separation, or rapidly non-stationary rates.

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。

Extract features from 1D signals using signalTimeFeatureExtractor, signalFrequencyFeatureExtractor, and signalTimeFrequencyFeatureExtractor. Use when computing time-domain features (amplitude, energy, shape factors), frequency-domain features (spectral location, power, bandwidth, PSD), or time-frequency features (spectral shape, instantaneous, ridges, wavelet, EMD-derived) on a per-frame basis. Use when the user asks to "extract features", "compute spectral features", "build a feature table for a classifier", "get per-frame statistics", "run feature extraction on this signal", or describes a vibration / biosignal / radar / sensor signal needing features for downstream ML or analysis. Includes optional GPU acceleration via canUseGPU and gpuArray. Does not cover filter design, audio-specific feature extraction (use audioFeatureExtractor in Audio Toolbox instead), batch dataset orchestration, or 2D / image features.

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 when generating video with Vidu models on Scenario via MCP: text-to-video, image-to-video from a single still, start and end frame interpolation, reference-to-video keeping characters and props consistent from up to 7 reference images or reference videos, anime-style motion, movement amplitude control, or deciding between Q3, Q2, Q1, and 2.0 tiers. Keywords: Vidu Q3 Pro, Q2 Turbo, Shengshu Technology, T2V, I2V, R2V, Reference2V, start end frame, background music toggle.

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。

How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt, and the failure modes that produce data nobody trusts. Triggers on product analytics setup, event taxonomy, tracking plan, instrumentation, schema versioning, North Star metric, retention cohorts, funnel design, naming conventions, instrument new feature, audit existing analytics, dashboard reconciliation, instrumentation debt, Mixpanel setup, Amplitude setup, PostHog setup, warehouse-native analytics. Also triggers when the team has data but cannot trust it, or when designing instrumentation for a new feature, or when auditing an existing setup that has drifted.

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。

A platform decision framework for experimentation. When to use Statsig vs PostHog vs GrowthBook vs Optimizely vs Amplitude vs Eppo vs Kameleoon. How to migrate between them. How to coordinate when multi-platform is genuinely warranted. The decisions that compound for years and the ones you can defer. Triggers on which experimentation platform, choose Statsig vs PostHog, evaluate experimentation tools, switch experimentation platform, migrate from Optimizely, consolidate experimentation tools, multi-platform experimentation, experimentation platform decision, ab test platform selection, feature flag platform vs experiment platform, warehouse-native experiments, vendor lock-in experimentation. Also triggers when a team is asking about cost, governance, or migration cost across experimentation tools, or when an evaluation is starting.

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。

Audit a project's infrastructure and SaaS stack, then produce a cost report showing what the user currently pays and what they would save by consolidating onto Temps (self-hosted or Temps Cloud). Detects hosting platforms (Vercel, Netlify, Railway, Render, Heroku, Fly.io), analytics (PostHog, Plausible, Mixpanel, Amplitude, Fathom), error tracking (Sentry, Bugsnag, Rollbar, Honeybadger), session replay (LogRocket, FullStory, Hotjar, Highlight), uptime monitoring (Pingdom, UptimeRobot, Better Stack, Checkly), managed databases (Supabase, Neon, PlanetScale, MongoDB Atlas, Upstash, RDS), and transactional email (SendGrid, Postmark, Resend, Mailgun) from dependencies, config files, and env var names. Use when the user wants to: (1) Know how much they would save by switching to Temps, (2) Audit their SaaS/infrastructure spend, (3) Compare their current stack's cost against self-hosting, (4) Decide whether Temps is worth it, (5) Build a business case for consolidating tools. Triggers: "how much would I save", "temps savings", "cost comparison", "audit my stack", "am I overpaying", "saas spend", "calculate savings", "is temps cheaper".

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。