「fish」の検索結果

CAPA system management for medical device QMS, covering root cause analysis, corrective action planning, and effectiveness verification. Use for CAPA investigations, 5-Why and fishbone analysis, and corrective action tracking.

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。

April Dunford's 10-step positioning methodology for B2B tech products. Use when customers don't understand the product, sales cycles are long because reps must "explain it," prospects compare you to wrong competitors, or you face price pressure despite a good product. Covers competitive alternatives, unique attributes, value themes, target market characteristics, market category (Head to Head / Big Fish Small Pond / New Game), and trend layering.

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。

Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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。

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 drivers.

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 peer review for analytical work. Use when reviewing teammates' analysis, providing constructive feedback, or establishing analysis quality standards.

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。

Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models for planning.

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。

Build structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML definitions compatible with dbt Semantic Layer or similar frameworks.

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。

Create concise executive summaries from detailed analysis. Use when preparing board decks, executive briefings, or condensing complex analysis into decision-ready formats for senior audiences.

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。

Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.

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。

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.

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。

Design specifications for effective dashboards. Use when planning new dashboards, improving existing ones, or documenting dashboard requirements before development starts.

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 requirements elicitation for analysis requests. Use when scoping new analysis projects, clarifying ambiguous business questions, or documenting analysis acceptance criteria with stakeholders.

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。

Efficiently package context for AI-assisted analysis. Use when preparing to work with Claude on analysis, organizing context documents, or structuring prompts for complex analytical tasks.

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。

SQL query review for correctness, performance, and best practices. Activate when a query needs review before production use, shows unexpected results, or runs too slowly.

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。

Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, comparing segments through a funnel, or optimizing user journeys.

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。

Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.

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。

Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.

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。

Translate SQL queries into plain language business logic. Use when documenting queries, explaining analysis to non-technical stakeholders, code reviewing for correctness, or building a query catalog.

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。

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。