數據科學

Business analysis skill for requirements gathering, market research, strategic analysis, and project brief creation. Use when users need to (1) Create comprehensive project briefs, (2) Conduct market or competitive analysis, (3) Facilitate brainstorming sessions, (4) Define problems and validate assumptions, (5) Elicit requirements through structured questioning, (6) Create user personas, or (7) Analyze project feasibility and scope

Analyzes seller lead performance, revenue attribution, and marketing campaigns for the Seller vertical (acquired business with separate data architecture). Use when working with seller leads, sell intent, EFR calculations, seller paid search, display/social, or B2C/B2B seller campaigns. Triggers include seller leads, sell_spend, sell_attribution, EFR, GQ leads, UCA, seller revenue, PMAX, DSA, brand campaigns, seller outage analysis, facebook ads, display ads.

Use OPAL subquery syntax (@labels) and union operations to combine multiple datasets or time periods. Essential for period-over-period comparisons, multi-dataset analysis, and complex data transformations. Covers @label <- @ syntax, timeshift for temporal shifts, union for combining results, and any_not_null() for collapsing grouped data.

Review unpushed commits before pushing for code quality, bugs, security issues, and error handling. Use when preparing to push commits, want pre-push code review, or need to validate changes before pushing. Runs comprehensive analysis using specialized review agents.

Conduct comprehensive, multi-source research on any topic using web search, documentation lookup, and critical analysis. This skill should be used when users request thorough investigation, deep research, or comprehensive analysis of topics including but not limited to AI systems, technology trends, academic subjects, business strategies, or current events. | 任意のトピックに対して、Web検索、ドキュメント参照、批判的分析を用いた包括的な調査を実施。徹底的な調査、詳細なリサーチ、包括的な分析が必要な場合や、AIシステム、技術トレンド、学術的テーマ、ビジネス戦略、時事問題などについて深く知りたい場合に使用。

Comprehensive post-build verification of Shannon Framework's application outputs using three-layer methodology: Flow Verification (execution trace analysis), Artifact Verification (physical output inspection), and Functional Verification (runtime testing). Verifies Shannon built production-ready applications across all domains (Frontend, Backend, Database, Mobile, DevOps). Ensures NO MOCKS compliance, cross-platform functionality, and complete integration. Use after: Shannon builds any application via /shannon:wave, need to verify build quality, production readiness assessment.

Download ERA5 climate reanalysis data from the Copernicus Climate Data Store using cdsapi. Use this skill when users request ERA5 data, climate forcing data, meteorological variables, or need to download atmospheric/land surface data for ecosystem modeling, climate analysis, or model validation.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions

Creates and edits Excel spreadsheets with formulas, formatting, and financial modeling standards. Use when working with .xlsx files, financial models, data analysis, or formula-heavy spreadsheets. Covers formula recalculation, color coding standards, and common pitfalls.

This skill optimizes database queries to fix N+1 problems, add indexes, and improve performance.Supports PostgreSQL, MySQL, MongoDB, Redis with ORMs (SQLAlchemy, Prisma, TypeORM, Mongoose).Detects slow queries, suggests indexes, rewrites inefficient queries, and adds eager loading.Activate when user says "optimize queries", "fix N+1", "slow database", "add indexes", or performance issues.Output: Optimized queries with indexes, eager loading, query analysis, and before/after benchmarks.

Analyze event datasets (logs) and intervals over time using OPAL timechart. Use when you need to visualize trends, track metrics over time, or create time-series charts. Covers timechart for temporal binning, bin duration options (1h, 5m, 1d), options(bins:N) for controlling bin count, and understanding temporal output columns (_c_valid_from, _c_valid_to, _c_bucket). Returns multiple rows per group for time-series visualization. For single summaries, see aggregating-event-datasets skill.

Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.

Systematic comparison of segments, cohorts, or time periods - ensure fair apples-to-apples comparisons, identify meaningful differences, explain WHY differences exist

Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_by for grouping, aggregation functions (count, sum, avg, percentile), and topk for top N results. Returns single summary row per group across entire time range. For time-series trends, see time-series-analysis skill.

Validates complete requirements traceability across EARS requirements → design → tasks → code → tests. Trigger terms: traceability, requirements coverage, coverage matrix, traceability matrix, requirement mapping, test coverage, EARS coverage, requirements tracking, traceability audit, gap detection, orphaned requirements, untested code, coverage validation, traceability analysis. Enforces Constitutional Article V (Traceability Mandate) with comprehensive validation: - Requirement → Design mapping (100% coverage) - Design → Task mapping - Task → Code implementation mapping - Code → Test mapping (100% coverage) - Gap detection (orphaned requirements, untested code) - Coverage percentage reporting - Traceability matrix generation Use when: user needs traceability validation, coverage analysis, gap detection, or requirements tracking across the full development lifecycle.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions | バグ、テスト失敗、予期しない動作に遭遇した際、修正を提案する前に使用 - 4フェーズフレームワーク（根本原因調査、パターン分析、仮説検証、実装）により、解決策を試す前に理解を確保

Advanced test reporting, quality dashboards, predictive analytics, trend analysis, and executive reporting for QE metrics. Use when communicating quality status, tracking trends, or making data-driven decisions.

Heart rate variability biometrics and emotional awareness training. Expert in HRV analysis, interoception training, biofeedback, and emotional intelligence. Activate on 'HRV', 'heart rate variability', 'alexithymia', 'biofeedback', 'vagal tone', 'interoception', 'RMSSD', 'autonomic nervous system'. NOT for general fitness tracking without HRV focus, simple heart rate monitoring, or diagnosing medical conditions (only licensed professionals diagnose).

Automates multi-model skill description analysis using sequential-thinking. Evaluates invocation likelihood (Sonnet 4.5, Haiku), authority level against competing prompts, semantic clarity, user request matching. Generates 0-100 ratings with actionable recommendations. Use when evaluating skill descriptions or saying 'evaluate skill', 'test skill description', 'analyze skill effectiveness'.

Pre-editing analysis for Whole documentation. Use when: (1) Starting new editing session, (2) Checking for duplicates across domains, (3) Analyzing section completeness, (4) Validating structure before bulk edits, (5) Generating analysis reports.