'ics' 검색 결과

Physics-based animation library combining React Spring (spring dynamics, gesture integration, 60fps animations) and Popmotion (low-level composable animation utilities, reactive streams). Use when building fluid, natural-feeling UI animations, gesture-driven interfaces, physics simulations, or spring-loaded interactions. Triggers on tasks involving React Spring hooks, spring physics, inertia scrolling, physics-based motion, animation composition, or natural UI movements. Alternative physics approach to motion-framer for more physically accurate animations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

POS analytics tracking patterns (WooPosAnalyticsEvent sealed class, WooPosAnalyticsTracker, WooPosAnalyticsEventConstant). Use when writing, editing, exploring, debugging, or reviewing analytics tracking in POS (WooPos*) code. NOT for main store app — use the `store-analytics` skill instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Think and work like an expert Geodynamics & Tectonics Scientist. Use when a task calls for Geodynamics & Tectonics Scientist judgment. Reasons from plate kinematics, lithospheric rheology, and interseismic-versus-coseismic strain partitioning through GAMIT/GLOBK and MintPy geodesy, Okada/viscoelastic slip inversion, OxCal paleoseismic chronologies, ASPECT mantle modeling, and OpenQuake/USGS NSHM hazard while treating InSAR atmospheric delay, monument instability, unmodeled postseismic afterslip, and incompatible-timescale rate stacking as first-class failure modes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive expertise in blockchain data analysis using Dune Analytics, custom indexers, and on-chain data querying. Covers SQL for blockchain, dashboard creation, protocol metrics, and alpha discovery. Use when "onchain analytics, Dune Analytics, blockchain data, SQL blockchain, protocol metrics, TVL tracking, wallet analysis, token analytics, DEX volume, dashboard, " mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in measuring what matters in communities. Covers health metrics, engagement analytics, sentiment analysis, cohort tracking, and reporting. Knows that good data drives good decisions, and bad metrics drive bad behavior. Use when "community metrics, community analytics, measure community, community health, engagement metrics, community reporting, " mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

World-class expertise in measuring, attributing, and optimizing AI-generated content performance. Combining data science rigor with content strategy intelligence to answer the questions traditional content analytics can't: Is AI content performing? Which AI variations convert? What's the true ROI of AI vs traditional content creation? This isn't vanity metrics for robots. This is the discipline of proving AI content drives business outcomes - and using that data to make AI content systems better, faster, and more profitable. Built on the principles of companies like Jasper, Copy.ai, and Notion who've scaled AI content operations with measurement as the foundation. Use when "ai content performance, ai content analytics, measure ai content, ai content roi, ai vs human content, ai content attribution, ai content testing, ai variation testing, ai content dashboard, ai content metrics, prompt performance, ai content conversion, ai content quality score, content velocity, ai content efficiency, ai-content, analytics, measurement, attribution, roi, ab-testing, performance, optimization, data-driven, content-analytics" mentioned.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ANALYTICS-PRODUCT — Decida com Dados workflow skill. Use this skill when the user needs Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analytics tracking, interpretation, funnel analysis, product metrics, and ROI measurement. Use when setting up GA4/GTM tracking, interpreting analytics data, analyzing conversion funnels, calculating ROI, or measuring product engagement. Triggers on "analytics," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "tracking plan," "funnel analysis," "conversion rates," "user flow," "cohort analysis," "retention," "product metrics," "North Star metric," "ROI," "break-even," "payback period," "investment analysis," "validate my funnel," "why isn't my funnel converting," or "executive financial report." For A/B test setup, see ab-test-setup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analytics reconnaissance — scan existing event tracking, metric definitions, dashboards, and analytics configuration to understand what is currently being measured. Use when asked to "what are we tracking", "audit our analytics", "what metrics exist", "analytics inventory", or before designing new metrics or instrumentation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Tokenomics Analysis Agent — supply mechanics, unlock schedules, vesting, inflation, staking economics, distribution fairness, and token utility with Tokenomics Score (0-100)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when a task needs the judgment of a Bioinformatics Scientist — designing a differential-expression or GWAS analysis with correct multiple-testing correction, calling and triaging variants from NGS data using genotype-likelihood confidence rather than raw allele counts, interpreting a BLAST/DIAMOND homology search, choosing or building a genomics pipeline (aligner, caller, normalization method) for a specific data-generating process, or judging whether a computational-biology analysis's statistical design actually supports the biological claim being made. Distinct from a bioinformatics-technician (executes and QC-gates an established pipeline someone else designed) and a data-scientist (general product/experimentation judgment, not genomics-specific statistics) — this role owns which method and statistical model a genomics question requires, and why.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze flow metrics to identify delivery bottlenecks and improve throughput. Use when the user says "flow metrics", "why is delivery slow", "cycle time analysis", "WIP analysis", "throughput review", "what's our lead time", "DORA metrics", "where is work getting stuck", "Kanban metrics", or wants to diagnose delivery performance using data - even if they don't explicitly say "flow metrics review". Based on "Making Work Visible" by Dominica DeGrandis (WIP limits, flow blockers, time thieves) and "Accelerate" by Forsgren, Humble & Kim (DORA metrics, delivery performance indicators).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a tracking plan from scratch, auditing existing analytics for gaps or errors, debugging missing events, or setting up GTM. Trigger keywords: GA4 setup, Google Tag Manager, GTM, event tracking, analytics implementation, conversion tracking, tracking plan, event taxonomy, custom dimensions, UTM tracking, analytics audit, missing events, tracking broken. NOT for analyzing marketing campaign data — use campaign-analytics for that. NOT for BI dashboards — use product-analytics for in-product event analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert-level dynamics covering kinematics and kinetics of particles and rigid bodies, Newton-Euler equations, energy methods, impulse-momentum, and vibrations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert-level genetics covering Mendelian inheritance, molecular genetics, mutations, linkage analysis, population genetics, epigenetics, and GWAS. Covers classical and modern approaches from Mendel to CRISPR.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ScalarDB Analytics設計エージェント - ScalarDB Analyticsを使用した分析基盤の設計。Apache Sparkベースの分析クエリ、マルチDB横断分析を策定。/design-scalardb-analytics [対象パス] で呼び出し。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ScalarDB Analytics設計エージェント - ScalarDB Analyticsを使用した分析基盤の設計。Apache Sparkベースの分析クエリ、マルチDB横断分析を策定。/design-scalardb-analytics [対象パス] で呼び出し。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assigns honest lipid annotation levels, designs class-based internal-standard quantification, and runs lipid-aware differential and enrichment analysis with lipidr, guarding against in-source-fragment phantoms, sn-position over-claims, and invalid cross-class quantification. Use when naming or canonicalizing lipid species (shorthand separators, Goslin), deciding shotgun vs RP vs HILIC LC-MS, picking internal standards (SPLASH/EquiSPLASH), interpreting MS-DIAL/LipidSearch output, or comparing lipid classes. For general feature detection see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for non-lipid annotation confidence see metabolomics/metabolite-annotation; for normalization/QC see metabolomics/normalization-qc; for multivariate stats see metabolomics/statistical-analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Work on lyrics — the web fallback (LRCLIB, Musixmatch, NetEase, Genius), the in-memory cache, verification, and embedding lyrics into audio file tags. Use this whenever a task touches `services/lyrics.py`, `services/tags.py`, the `/lyrics.json` route, or `scripts/embed_lyrics.py` / `embed_lyrics_cron.sh` — adding or fixing a provider, changing caching/verification, or the batch tagger. Covers the provider contract, the synced-vs-plain rule, the read-only cache, and why writes only ever store plain text.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan product analytics with event tracking, conversion funnels that reveal drop-offs, and retention analysis by user group. Includes PostHog examples and guidance for evaluating A/B tests, defining a core product metric, and designing dashboards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing events to track product usage
• Investigating drop-offs before signup or purchase
• Comparing weekly retention across user groups

Analyze customer profitability and revenue durability for SaaS, subscription, and other recurring businesses. Examine acquisition costs, retention, and revenue trends by customer group, then produce an Excel workbook, summary slide, and issues for further investigation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing recurring revenue quality in an investment target
• Tracking revenue by customer acquisition year
• Comparing acquisition costs with customer lifetime value

Plan technology strategy and engineering team growth with frameworks for technical debt, hiring, architecture decisions, and vendor evaluation. Use Python tools and decision templates to prioritize improvements, structure teams, and track engineering performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prioritizing technical debt reduction
• Planning hiring and engineering team structure
• Comparing technologies and vendors