'ics' 검색 결과

Expert-level optics knowledge. Use when working with geometric optics, wave optics, interference, diffraction, polarization, lasers, fiber optics, optical instruments, or photonics. Also use when the user mentions 'reflection', 'refraction', 'Snell law', 'lens', 'mirror', 'interference', 'diffraction', 'polarization', 'laser', 'fiber optic', 'holography', 'aberration', or 'optical instrument'.

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Expert-level acoustics knowledge. Use when working with sound waves, acoustic propagation, resonance, standing waves, Doppler effect, room acoustics, ultrasound, noise control, musical acoustics, or architectural acoustics. Also use when the user mentions 'sound wave', 'decibel', 'frequency', 'resonance', 'standing wave', 'Doppler effect', 'acoustic impedance', 'reverberation', 'ultrasound', 'noise control', 'harmonics', or 'sound intensity'.

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ScalarDB Cluster および ScalarDB Analytics のアーキテクチャ、サイジング、構成を見積もるスキル。 性能要件、可用性要件、クラウド環境からScalarDB Cluster Pod数、Kubernetes構成、 バックエンドDB、API Gateway、監視システム等の全体構成を見積もる。 ScalarDB Analyticsを使用する場合はEMR/Databricksのサイジングも含む。 使用タイミング: - 「ScalarDBのサイジングを見積もりたい」「ScalarDB環境を構築したい」 - 「ScalarDB Clusterの構成を決めたい」「ScalarDBの費用を算出したい」 - 「開発/テスト/ステージング/本番環境のScalarDB構成」 - CI/CD、Blue/Green、Canary Deploymentを含む本番環境設計 - 「ScalarDB Analyticsを使いたい」「分析クエリ環境を構築したい」 - 「EMR/Databricksのサイジングを見積もりたい」 出力: Markdown形式の見積もり結果 + HTML形式のレポート 費用: USD/JPY両建て（為替レート明記）

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リポジトリ全体を IEEE-CS/ACM ソフトウェア工学倫理綱領（8原則・80項目）とダークパターン 観点で監査し、証跡付きで採点（合格/要改善/不適合）する。多エージェント敵対的監査により 誤検知（ethical grandstanding）を検証で潰す。結果は自己監査のツイート下書きにできる。 トリガー: 倫理監査, 技術者倫理チェック, ethics audit, 倫理チェック, /ethics-audit

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When the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For retention metrics, see retention-optimization.

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Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

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Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.

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Solve quantitative problems in biophysics — pharmacokinetics (PK volume of distribution, clearance, half-life), epidemiology (R0, attack rate), toxicology (LD50, NOAEL), population genetics (Hardy-Weinberg, Fst), enzyme kinetics (Michaelis-Menten), thermodynamics. Use for first-principles quantitative biology calculations, dose calculations, exposure assessment, and biophysical-property estimation.

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Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.

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Designs and validates quantitative targeted metabolomics assays (MRM/SRM on triple-quadrupole, PRM on high-resolution instruments) to report absolute concentrations. Covers the internal-standard strategy (external cal -> global IS -> standard addition -> stable-isotope-labeled IS), weighted calibration judged by back-calculated %RE not R-squared, ion-ratio quantifier/qualifier confirmation, matrix-effect/recovery characterization, and ICH M10 method validation. Use when quantifying a closed panel of known metabolites with units, building or validating an LC-MS/MS assay, choosing an IS or calibration weighting, or judging whether a reported concentration is trustworthy. For untargeted feature detection see metabolomics/xcms-preprocessing; for group statistics see metabolomics/statistical-analysis; for flux/MID/tracing see metabolomics/isotope-tracing.

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Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and molecular networking, and assigns a defensible MSI/Schymanski confidence level to each. Use when naming detected features, scoring MS/MS against a reference library, running SIRIUS, or deciding what confidence level an evidence set actually supports. For upstream feature extraction see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for downstream enrichment that must respect these levels see metabolomics/pathway-mapping; for lipid-specific structural annotation see metabolomics/lipidomics.

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Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave/grouping/alignment parameters. For drift correction and QC/CV filtering execution see metabolomics/normalization-qc; for metabolite identification see metabolomics/metabolite-annotation; for the MS-DIAL GUI alternative with MS2Dec deconvolution see metabolomics/msdial-preprocessing; for downstream statistics see metabolomics/statistical-analysis.

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Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `/numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).

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Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `$numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).

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Gateway to 400+ bioinformatics skills from bioSkills and ClawBio. Covers genomics, transcriptomics, single-cell, variant calling, pharmacogenomics, metagenomics, structural biology, and more. Fetches domain-specific reference material on demand.

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Implement AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.

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여러 소셜 플랫폼의 지표를 하나의 HTML 분석 대시보드로 만듭니다. 그래프 상세 정보와 기간 비교, 밝고 어두운 테마 전환을 제공하고 디자인 지침을 반영합니다.

• 여러 소셜 플랫폼 지표를 한곳에서 확인
• 그래프에서 선택한 기간 비교
• 그래프의 각 지점과 분석 내용 확인

Prepare constructive review drafts for scientific manuscripts, protocols, preprints, and proposals. Organize checks of claims, evidence, methods, statistics, reproducibility, ethics, figures, and citations into specific comments that a responsible human reviewer can verify and revise.

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• Draft evidence-based comments on a scientific manuscript
• Check methods, statistics, and reproducibility reporting
• Choose reporting guidelines and identify missing information

Write and review SQL for reports and metrics in a data warehouse, a store of analytical data. Applies rules for explicit columns, date filters, aggregate names, and limited exploratory results, returning SQL with a brief explanation.

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• Write SQL for report and dashboard metrics
• Calculate counts, sums, and rolling metrics
• Combine data from multiple tables

Design KPI dashboards that help teams make decisions using clearly defined performance measures. Choose relevant metrics, organize summary and detail views, document calculations, and address inconsistent figures, database load, and excessive alerts.

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• Design a SaaS executive metrics dashboard
• Build a live service health monitoring view
• Analyze retention by signup cohort

Build a Solution Blueprint for a Dynamics 365 Finance and Supply Chain Management implementation through guided interviews. Define scope and architecture, compare options, and document decisions, their rationale, assumptions, and unresolved questions across working sessions.

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• Create an architecture blueprint for a new D365 implementation
• Define implementation scope and rollout phases
• Compare data migration and integration approaches

Analyze RAM dumps with Volatility 3 to investigate suspicious processes, network connections, injected code, and credential theft. Guides memory capture and evidence extraction, then organizes findings into a forensic report for incident response.

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• Investigating malware that runs only in memory
• Checking suspicious processes and network connections
• Investigating signs of credential theft

Build normal traffic profiles for industrial control networks and identify unusual connections, polling intervals, and protocol operations. Use machine learning and process records to investigate deviations and produce anomaly reports with severity levels.

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• Establishing normal traffic profiles for industrial networks
• Detecting unusual connections and polling intervals
• Investigating Nozomi Guardian or Dragos alerts

Build repeatable business reports from connected data sources or uploaded CSV and XLSX files. Define metrics and comparisons, check the calculations, deliver a concise summary and Excel workbook, and save the report definition for future or scheduled runs.

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• Comparing sales by location with last year
• Reviewing overdue receivables by age
• Tracking labor costs as a percentage of revenue