Analyst prompts

You are a data analyst specializing in user behavior and retention analysis. Design a cohort analysis plan for {{product_type}} to analyze {{retention_metric}}. Create a comprehensive cohort analysis framework with: 1. **Cohort Definition**: Define {{cohort_definition}} with: - Cohort grouping criteria (signup date, first purchase, first login, etc.) - Minimum cohort size for statistical validity - Exclusion criteria (test accounts, bots, etc.) 2. **Retention Metric**: Define {{retention_metric}...

Write an executive summary of this analysis: {{analysis_results}} Audience: {{audience}} Decision to be made: {{decision}} Structure: 1. Key finding (1 sentence, bold) 2. Context (2-3 sentences on why this matters) 3. Top 3 insights with supporting data 4. Recommendations (specific, actionable) 5. Risks of inaction 6. Next steps Rules: - Lead with the "so what" — what should the audience DO? - Use plain language, not technical jargon - Numbers should be rounded and contextualized - Total length:...

You are a data visualization expert and analytics consultant. Design an analytics dashboard for {{dashboard_purpose}} targeting {{target_users}}. Create a comprehensive dashboard design brief with: 1. **Dashboard Objectives**: Define the primary goals, key questions the dashboard should answer, and success metrics. 2. **Key Performance Indicators**: Identify and define {{key_metrics}} including: - Metric name and business definition - Data source and calculation method - Benchmark targets and th...

You are a data engineer specializing in data pipeline architecture. Write a specification for a data pipeline that processes {{data_sources}} with the following requirements: {{transformation_requirements}}. Create a comprehensive pipeline specification with: 1. **Pipeline Overview**: - Purpose and business context - Data flow diagram description (source -> transform -> destination) - Processing frequency and latency requirements 2. **Data Sources**: Detail {{data_sources}}: - Source systems, fo...

You are an expert at structured data extraction. Extract key information from this research paper. <paper> {{paper_text}} </paper> Extract: { "title": "string", "authors": ["name1", "name2"], "abstract": "string (max 200 words)", "keywords": ["keyword1", "keyword2"], "methodology": "string", "key_findings": ["finding1", "finding2"], "limitations": ["limitation1", "limitation2"], "publication_year": "YYYY or null" } Only extract information that is explicitly stated in the paper. Do not infer or ...

I want you to act as a fallacy finder. You will be on the lookout for invalid arguments so you can call out any logical errors or inconsistencies that may be present in statements and discourse. Your job is to provide evidence-based feedback and point out any fallacies, faulty reasoning, false assumptions, or incorrect conclusions. Statement: {{statement}} For each fallacy found: 1. Name the fallacy 2. Quote the specific text 3. Explain why it's a fallacy 4. Suggest how to strengthen the argumen...

You are a senior growth analyst specializing in retention. Design a churn analysis plan for a {{product_type}} product. Available data: {{data_available}}. Retention goal: {{retention_goal}}. Include: (1) a churn definition with time-based thresholds, (2) a prioritized list of behavioral and demographic churn predictors, (3) a segmentation strategy with at least 3 risk tiers, (4) recommended statistical methods with justification, and (5) two retention experiments targeting the highest-risk segm...

You are an experimentation specialist designing an A/B test. Hypothesis: {{hypothesis}}. Primary metric: {{primary_metric}} (baseline: {{baseline_value}}). Minimum detectable effect: {{mde}}. Daily traffic: {{daily_traffic}}. Design a complete experiment plan: (1) formal null/alternative hypothesis, (2) sample size calculation (80% power, 95% confidence), (3) estimated test duration, (4) 2-3 guardrail metrics, (5) potential confounders and controls, (6) ship/iterate/kill decision framework.

Build a retention analysis framework for {{product_type}}. Cohorts by {{cohort_criteria}}, tracking {{retention_metrics}} over {{time_horizon}}. Produce: (1) cohort definition with inclusion/exclusion criteria, (2) SQL/pandas to build cohort table, (3) retention table template with example data, (4) three visualization specs (curve, heatmap, rolling comparison), (5) interpretation guide with actionable insight patterns.

Build an LTV model for a {{business_model}} company. Segments: {{customer_segments}}. Data: {{available_data}}. Use case: {{use_case}}. Design: (1) appropriate LTV formula with justification, (2) segment-level calculation table, (3) sensitivity analysis (+/-20%), (4) SQL/pseudocode for calculation, (5) validation plan.

You are a senior data analyst conducting a data quality audit. Analyze the {{data_source}} dataset named {{dataset_name}} covering the {{time_period}} period. Focus on the following key columns: {{key_columns}}. For each column, identify potential quality issues across five dimensions: completeness (nulls, blanks), consistency (format mismatches, conflicting values), accuracy (outliers, impossible ranges), timeliness (staleness, lag), and uniqueness (duplicates). Output a prioritized checklist w...

You are a conversion rate optimization analyst. I have a {{funnel_name}} funnel with these stages: {{stages}}. Here are the conversion numbers for each stage: {{conversion_data}}. The business goal is {{business_goal}}. Analyze the drop-off between each consecutive stage, calculate the stage-to-stage conversion rates and cumulative conversion rate. Identify the single biggest bottleneck stage. For that bottleneck, generate 3-5 specific hypotheses explaining why users drop off, categorized by: UX...

You are a localization specialist with analytics expertise. Localize this report for {{target_market}}: {{report_content}}. Apply: (1) terminology adaptation to {{terminology_standards}}, (2) date/number/currency format conversion, (3) cultural adjustment for {{cultural_notes}}, (4) chart label localization, (5) flag benchmarks needing recalculation. Output the localized report with a changelog.

Design a dashboard schema for {{dashboard_purpose}}. Data sources: {{data_sources}}. User: {{user_role}}. Refresh: {{refresh_frequency}}. Design: (1) star schema with fact/dimension tables, (2) metric definition sheet, (3) filter/cross-filter spec, (4) wireframe layout description, (5) row-level security rules, (6) performance optimization notes.

Create a data visualization style reference showing {{chart_type}} charts in a {{visual_style}} aesthetic. Colors: {{brand_colors}} ({{color_count}} distinct). Data: {{data_category}}. Show 4 variations: (1) minimal for executives, (2) detailed for analysts, (3) dark-mode for dashboards, (4) print-friendly. Consistent typography, grid, legend, and annotation style. --ar 16:9 --v 6.1

Write an insights report for {{stakeholder_group}}. Dataset: {{dataset_name}}. Findings: {{key_findings}}. Business impact: {{business_impact}}. Include: (1) executive summary, (2) findings with data/context/implication, (3) recommended visualizations, (4) risks and limitations, (5) prioritized next steps with owners and timelines, (6) appendix outline. Direct, analytical tone.

Estimate market size for {{market_definition}} in {{geography}}. Methodology: {{methodology}}. Assumptions: {{assumptions}}. Build: (1) top-down TAM/SAM/SOM, (2) bottom-up from unit economics, (3) reconciliation, (4) sensitivity table (optimistic/base/pessimistic), (5) top 5 assumptions with validation methods, (6) data sources.

Design a KPI dashboard for {{team}} at {{company}}. Business objectives: {{objectives}} Current reporting: {{current_reporting}} Audience: {{audience}} For each KPI: 1. Metric name and definition 2. Data source and calculation formula 3. Visualization type (chart, gauge, table) 4. Target/benchmark 5. Update frequency 6. Drill-down dimensions Organize into logical sections. Include alert thresholds for critical metrics. Make it actionable, not just informational.

You are a financial data analyst designing a reconciliation process. Source A: {{source_a}}. Source B: {{source_b}}. Discrepancy scope: {{discrepancy_scope}}. Materiality threshold: {{materiality_threshold}}. Design: (1) reconciliation matching logic (exact, fuzzy, date-window), (2) discrepancy classification taxonomy (timing, FX, missing records, duplicates, rounding), (3) SQL queries for each reconciliation step, (4) investigation workflow with escalation rules, (5) reporting template for the ...

You are a senior analyst specializing in executive communications. Write an executive summary for {{report_title}} based on the following findings: {{key_findings}}. Create a compelling executive summary with: 1. **Opening Statement**: Hook the audience with the single most critical insight or finding, framed as a business implication. 2. **Key Findings**: Present {{key_findings}} in order of business impact: - Finding statement (1 sentence) - Supporting data point - Business implication 3. **St...

You are a data analyst specializing in anomaly detection. Design an anomaly detection plan for {{metric_name}} in the {{data_domain}} domain. Data characteristics: {{data_characteristics}}. Business context: {{business_context}}. Include: (1) recommended detection method (statistical, ML-based, or hybrid) with justification, (2) threshold strategy with sensitivity tuning, (3) false positive mitigation approach, (4) alert routing with severity tiers and escalation rules, (5) SQL or Python pseudoc...

You are a research analyst planning a survey analysis. Survey: {{survey_name}}. Respondents: {{respondent_count}}. Question types: {{question_types}}. Research objectives: {{research_objectives}}. Design: (1) data cleaning and validation rules, (2) open-ended response coding framework, (3) statistical test recommendations per question type, (4) cross-tabulation and segmentation strategy, (5) visualization plan with chart types, (6) significance and confidence level guidance, (7) reporting struct...