GitHub
codex-cli
OpenAI Codex CLIを起動・操作するスキル。マルチエージェント連携、コード生成の委譲、サブエージェントとしてCodexを活用したいときに使う。
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数据与 AI AI 技能
发现 数据与 AI 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
分类目录: 数据与 AI
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GitHub
handover
保守・運用引継ぎ書を作成する。運用移行、引き継ぎ時に使う。
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GitHub
systematic-debugging
体系的デバッグスキル。根本原因調査を先に行い、修正は後に行う規律あるアプローチ。
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GitHub
stakeholder-register
ステークホルダー登録簿を作成する。関係者分析、ステークホルダー管理時に使う。
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GitHub
analysis-retrospective
Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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GitHub
peer-review-template
Structured peer review for analytical work. Use when reviewing teammates' analysis, providing constructive feedback, or establishing analysis quality standards.
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GitHub
sql-to-business-logic
Translate SQL queries into plain language business logic. Use when documenting queries, explaining analysis to non-technical stakeholders, code review, or creating user-friendly query descriptions.
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GitHub
data-catalog-entry
Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
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GitHub
methodology-explainer
Explain analysis methodology to diverse audiences. Use when documenting 'how we did this' sections, building trust through transparency, or teaching analytical approaches to stakeholders.
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GitHub
time-series-analysis
Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models.
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GitHub
segmentation-analysis
Customer/user segmentation with actionable insights. Use when identifying distinct customer groups, analyzing segment-specific behavior, profiling high-value segments, or testing segmentation hypotheses.
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GitHub
stakeholder-requirements-gathering
Structured requirements elicitation for analysis requests. Use when scoping new analysis projects, clarifying ambiguous business questions, or documenting analysis acceptance criteria with stakeholders.
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GitHub
root-cause-investigation
Systematic investigation of metric changes and anomalies. Use when a metric unexpectedly changes, investigating business metric drops, explaining performance variations, or drilling into aggregated metric drive...
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GitHub
executive-summary-generator
Create concise executive summaries from detailed analysis. Use when preparing board decks, executive briefings, or condensing complex analysis into decision-ready formats.
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GitHub
funnel-analysis
Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, A/B testing funnel performance, or optimizing user journeys.
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GitHub
visualization-builder
Create effective, publication-ready data visualizations. Use when choosing chart types, designing dashboards, creating presentation visuals, or building interactive visualizations with best practices.
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GitHub
analysis-assumptions-log
Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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GitHub
analysis-qa-checklist
Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.
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GitHub
insight-synthesis
Transform data findings into compelling insights. Use when converting analysis results into actionable insights, connecting findings to business impact, or preparing insights for stakeholder communication.
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GitHub
schema-mapper
Database schema understanding and relationship mapping. Use when exploring unfamiliar databases, documenting table relationships, identifying join paths, or generating ERD documentation for existing schemas.
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GitHub
metric-reconciliation
Cross-source metric validation and discrepancy investigation. Use when metrics from different sources don't match, investigating data quality issues between systems, or validating data migration accuracy.
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GitHub
analysis-documentation
Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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GitHub
analysis-planning
Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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GitHub
ab-test-analysis
Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
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默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。