「metric」の検索結果

Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.

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Generate a full campaign brief with objectives, audience, messaging, channel strategy, content calendar, and success metrics. Use when planning a product launch, lead-gen push, or awareness campaign, when you need a week-by-week content calendar with dependencies, or when translating a marketing goal into a structured, executable plan.

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。

"Calculate and benchmark social media engagement rates across platforms and variants. Use this skill when the user needs to compute engagement metrics, compare performance across accounts or posts, or set engagement benchmarks — even if they say 'what is my engagement rate', 'benchmark engagement', or 'social media KPIs'.".

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。

"Measure social media influence using engagement-weighted metrics beyond follower count. Use this skill when the user needs to evaluate influencer effectiveness, compare influence across accounts, or build an influence scoring system — even if they say 'who is more influential', 'influencer ranking', or 'measure social impact'.".

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。

Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.

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。

"Evaluate backlink quality using Domain Authority, Domain Rating, and trust metrics. Use this skill when the user needs to assess link profile health, identify toxic backlinks, or plan link building strategy — even if they say 'check my backlinks', 'link building', or 'domain authority analysis'.".

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。

"Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics. Use this skill when the user needs to build a product ranking system beyond text relevance, balance relevance with commercial objectives, or implement learning-to-rank — even if they say 'product sorting', 'search result ranking', or 'how to rank products'.".

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。

"Calculate network centrality metrics to identify important nodes in graphs. Use this skill when the user needs to find key influencers, critical infrastructure nodes, or central actors in a network — even if they say 'who is most important in this network', 'key nodes', or 'network influence measurement'.".

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。

Analyze test coverage with detailed metrics, identify gaps, and generate comprehensive reports. Use when measuring code coverage, finding untested code, or validating coverage thresholds.

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。

Format recurring KPI snapshots, exception flags, and owner follow-ups for weekly operations reviews. Use when the work is routine packaging of operational metrics and exceptions. Do not use for root cause analysis, process redesign, or full performance diagnosis.

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。

Calculate and interpret Earned Value Management (EVM) metrics per PMBOK 8 Measurement Performance Domain. Compute SPI, CPI, EAC, ETC, TCPI, and generate variance narratives with corrective action recommendations. Use during project execution to assess schedule and cost performance.

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。

Design KPI dashboards with SMART metrics, visualization selection, alert thresholds, and refresh cadence. Covers leading vs lagging indicators, data dictionary, and stakeholder-specific views. Use when building operational dashboards, executive scorecards, or team performance views.

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。

Build change management plans using ADKAR and Kotter's 8-Step framework. Covers stakeholder readiness, communication plans, training schedules, resistance management, and adoption metrics. Use when rolling out new systems, reorganizations, or process changes that affect people.

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。

Design rigorous A/B test plans with hypothesis, sample size calculation, Minimum Detectable Effect (MDE), randomization strategy, and decision rules. Includes guardrail metrics and rollout playbook. Use when planning product experiments, conversion optimization, or data-driven feature decisions.

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。

Analyze Kanban flow metrics to identify bottlenecks and improve team throughput. Covers Cycle Time, Throughput, WIP, and Flow Efficiency using Little's Law and Theory of Constraints. Use when a team wants to optimize delivery flow or improve predictability. Do not use for sprint-based Scrum planning — use sprint-planning-assistant instead.

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。

Turn business data, KPI movement, or experiment results into a clear narrative using the what-so what-now what structure, audience calibration, and action-oriented insight. Use when leaders need a data-backed brief, dashboard storyline, or metric interpretation. Do not use for raw statistical modeling with no communication deliverable.

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 writing or reviewing request handlers, RPCs, or background jobs for production; adding tracing, metrics, or structured-log calls; or making diagnosability decisions

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。

Frappe Insights and analytics guidance for dashboards, query design, charts, permissions, metrics, ERPNext reporting, and operational analytics. Use when work touches Frappe Insights or analytics in the Frappe ecosystem.

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。

Secure storage, certificate pinning, biometric auth, jailbreak detection, code obfuscation, network security, screenshot prevention for React Native

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。

Baseline, half-leading, and font-face resolution for Label and captured (browser-imported) text — the arithmetic that decides where a glyph lands and how wide the box must be, plus the measure-vs-paint divergences that make text clip or sit low without any test going red.

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。

The measurement surface for ALL Pulp DSP and audio-pipeline work — read it BEFORE writing or gating DSP, not only when something already sounds wrong. Covers the C++ harness (signal generators, metrics, assertions, RenderScenario, contracts), the offline Audio Doctor (magnitude/frequency response, THD/THD+N, phase/group delay), and their Python sibling the Audio Quality Lab (tools/audio/quality-lab — null residual + alignment, LTAS log-spectral distance, spectral flux/centroid, HNR, Theil-Sen drift slope, Kaiser-sinc resampling, license-guarded corpus, regression-net ratchet). TRIGGER on AUTHORING work — "build/design an oscillator/filter/synth/effect", "add a DSP module", "what should the acceptance gate be", "how do I measure aliasing / anti-aliasing / alias floor", "null against a reference", "is this DSP correct", "choose a tolerance", "golden/regression corpus for audio", "measure drift or jitter", "A/B two renders" — AND on DEBUGGING work — "is there sound / no audio / I hear nothing", "does this filter/compressor/synth/delay produce the right signal", "prove the DSP / prove the contract", "measure the frequency response", "what's the THD / is it distorting", "what's the group delay / phase response / measured latency", "magnitude response curve", "render a test tone and assert", "audio regression", "64-frame works but 128 is silent", "sample-rate change pitch-shifted it", "describe what's in this buffer", "audio doctor", "compare before/after a DSP refactor". Reach for this BEFORE hand-rolling any FFT, null test, alias measurement, pitch tracker, or golden-render script — most of it already exists in one of the two lanes. Test/tool layer over HeadlessHost — deterministic, no audio device, no speakers. Off the realtime thread entirely.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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。

Generates a comprehensive milestone progress review including feature completeness, quality metrics, risk assessment, and go/no-go recommendation. Use at milestone checkpoints or when evaluating readiness for a milestone deadline.

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 a task needs senior data-scientist judgment — designing or reading out an experiment, investigating a metric move, deciding whether a causal claim holds, or judging whether a model or analysis actually supports the decision it's being used for.

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。