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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Before sharing any analysis output with a stakeholder — dashboard, report, ad-hoc query result, model output, or written findings. Run this every time, not just for big projects. The cost of a post-delivery correction is always higher than the cost of a pre-delivery check.
scripts/qa_runner.py against the output file to catch numeric, structural, and formatting issues programmatically.references/qa_checklist_master.md section by section: question framing, data sourcing, transformations, statistical validity, findings, and presentation.references/common_analysis_errors.md; pay special attention to the top-frequency mistakes for the analysis type.assets/qa_signoff_template.md with reviewer, issues found, resolution status, and delivery decision.qa_signoff_template.md confirming delivery readinessまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
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。
Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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。
Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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。
Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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。
Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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。
Standard business metric calculation with industry benchmarks. Use when calculating SaaS metrics (MRR, churn, LTV, CAC), e-commerce KPIs, or product analytics metrics with proper definitions.
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。