/analyze - Answer Data Questions
Answer a data question, from a quick lookup to a full analysis to a formal report.
Usage
/analyze <natural language question>
Workflow
1. Understand the Question
Parse the user's question and determine:
- Complexity level:
- Quick answer: Single metric, simple filter, factual lookup (e.g., "How many users signed up last week?")
- Full analysis: Multi-dimensional exploration, trend analysis, comparison (e.g., "What's driving the drop in conversion rate?")
- Formal report: Comprehensive investigation with methodology, caveats, and recommendations (e.g., "Prepare a quarterly business review of our subscription metrics")
- Data requirements: Which tables, metrics, dimensions, and time ranges are needed
- Output format: Number, table, chart, narrative, or combination
2. Gather Data
If a data warehouse MCP server is connected:
- Explore the schema to find relevant tables and columns
- Write SQL query(ies) to extract the needed data
- Execute the query and retrieve results
- If the query fails, debug and retry (check column names, table references, syntax for the specific dialect)
- If results look unexpected, run sanity checks before proceeding
If no data warehouse is connected:
- Ask the user to provide data in one of these ways:
- Paste query results directly
- Upload a CSV or Excel file
- Describe the schema so you can write queries for them to run
- If writing queries for manual execution, use the
sql-queries skill for dialect-specific best practices
- Once data is provided, proceed with analysis
3. Analyze
- Calculate relevant metrics, aggregations, and comparisons
- Identify patterns, trends, outliers, and anomalies
- Compare across dimensions (time periods, segments, categories)
- For complex analyses, break the problem into sub-questions and address each
4. Validate Before Presenting
Before sharing results, run through validation checks:
- Row count sanity: Does the number of records make sense?
- Null check: Are there unexpected nulls that could skew results?
- Magnitude check: Are the numbers in a reasonable range?
- Trend continuity: Do time series have unexpected gaps?
- Aggregation logic: Do subtotals sum to totals correctly?
If any check raises concerns, investigate and note caveats.
5. Present Findings
For quick answers:
- State the answer directly with relevant context
- Include the query used (collapsed or in a code block) for reproducibility
For full analyses:
- Lead with the key finding or insight
- Support with data tables and/or visualizations
- Note methodology and any caveats
- Suggest follow-up questions
For formal reports:
- Executive summary with key takeaways
- Methodology section explaining approach and data sources
- Detailed findings with supporting evidence
- Caveats, limitations, and data quality notes
- Recommendations and suggested next steps
6. Visualize Where Helpful
When a chart would communicate results more effectively than a table:
- Use the
data-visualization skill to select the right chart type
- Generate a Python visualization or build it into an HTML dashboard
- Follow visualization best practices for clarity and accuracy
Examples
Quick answer:
/analyze How many new users signed up in December?
Full analysis:
/analyze What's causing the increase in support ticket volume over the past 3 months? Break down by category and priority.
Formal report:
/analyze Prepare a data quality assessment of our customer table -- completeness, consistency, and any issues we should address.
Tips
- Be specific about time ranges, segments, or metrics when possible
- If you know the table names, mention them to speed up the process
- For complex questions, the agent may break them into multiple queries
- Results are always validated before presentation -- if something looks off, the agent will flag it
1---2name: analyze3description: Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.4---56# /analyze - Answer Data Questions789Answer a data question, from a quick lookup to a full analysis to a formal report.1011## Usage1213```14/analyze <natural language question>15```1617## Workflow1819### 1. Understand the Question2021Parse the user's question and determine:2223- **Complexity level**:24 - **Quick answer**: Single metric, simple filter, factual lookup (e.g., "How many users signed up last week?")25 - **Full analysis**: Multi-dimensional exploration, trend analysis, comparison (e.g., "What's driving the drop in conversion rate?")26 - **Formal report**: Comprehensive investigation with methodology, caveats, and recommendations (e.g., "Prepare a quarterly business review of our subscription metrics")27- **Data requirements**: Which tables, metrics, dimensions, and time ranges are needed28- **Output format**: Number, table, chart, narrative, or combination2930### 2. Gather Data3132**If a data warehouse MCP server is connected:**33341. Explore the schema to find relevant tables and columns352. Write SQL query(ies) to extract the needed data363. Execute the query and retrieve results374. If the query fails, debug and retry (check column names, table references, syntax for the specific dialect)385. If results look unexpected, run sanity checks before proceeding3940**If no data warehouse is connected:**41421. Ask the user to provide data in one of these ways:43 - Paste query results directly44 - Upload a CSV or Excel file45 - Describe the schema so you can write queries for them to run462. If writing queries for manual execution, use the `sql-queries` skill for dialect-specific best practices473. Once data is provided, proceed with analysis4849### 3. Analyze5051- Calculate relevant metrics, aggregations, and comparisons52- Identify patterns, trends, outliers, and anomalies53- Compare across dimensions (time periods, segments, categories)54- For complex analyses, break the problem into sub-questions and address each5556### 4. Validate Before Presenting5758Before sharing results, run through validation checks:5960- **Row count sanity**: Does the number of records make sense?61- **Null check**: Are there unexpected nulls that could skew results?62- **Magnitude check**: Are the numbers in a reasonable range?63- **Trend continuity**: Do time series have unexpected gaps?64- **Aggregation logic**: Do subtotals sum to totals correctly?6566If any check raises concerns, investigate and note caveats.6768### 5. Present Findings6970**For quick answers:**71- State the answer directly with relevant context72- Include the query used (collapsed or in a code block) for reproducibility7374**For full analyses:**75- Lead with the key finding or insight76- Support with data tables and/or visualizations77- Note methodology and any caveats78- Suggest follow-up questions7980**For formal reports:**81- Executive summary with key takeaways82- Methodology section explaining approach and data sources83- Detailed findings with supporting evidence84- Caveats, limitations, and data quality notes85- Recommendations and suggested next steps8687### 6. Visualize Where Helpful8889When a chart would communicate results more effectively than a table:9091- Use the `data-visualization` skill to select the right chart type92- Generate a Python visualization or build it into an HTML dashboard93- Follow visualization best practices for clarity and accuracy9495## Examples9697**Quick answer:**98```99/analyze How many new users signed up in December?100```101102**Full analysis:**103```104/analyze What's causing the increase in support ticket volume over the past 3 months? Break down by category and priority.105```106107**Formal report:**108```109/analyze Prepare a data quality assessment of our customer table -- completeness, consistency, and any issues we should address.110```111112## Tips113114- Be specific about time ranges, segments, or metrics when possible115- If you know the table names, mention them to speed up the process116- For complex questions, the agent may break them into multiple queries117- Results are always validated before presentation -- if something looks off, the agent will flag it
clawpod-app