anthropics--knowledge-work-plugins
wehub-resource-sync
120 行
4.3 KiB
Markdown
120 行
4.3 KiB
Markdown
---
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name: analyze
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description: 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.
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argument-hint: "<question>"
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---
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# /analyze - Answer Data Questions
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> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
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Answer a data question, from a quick lookup to a full analysis to a formal report.
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## Usage
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```
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/analyze <natural language question>
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```
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## Workflow
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### 1. Understand the Question
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Parse the user's question and determine:
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- **Complexity level**:
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- **Quick answer**: Single metric, simple filter, factual lookup (e.g., "How many users signed up last week?")
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- **Full analysis**: Multi-dimensional exploration, trend analysis, comparison (e.g., "What's driving the drop in conversion rate?")
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- **Formal report**: Comprehensive investigation with methodology, caveats, and recommendations (e.g., "Prepare a quarterly business review of our subscription metrics")
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- **Data requirements**: Which tables, metrics, dimensions, and time ranges are needed
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- **Output format**: Number, table, chart, narrative, or combination
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### 2. Gather Data
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**If a data warehouse MCP server is connected:**
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1. Explore the schema to find relevant tables and columns
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2. Write SQL query(ies) to extract the needed data
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3. Execute the query and retrieve results
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4. If the query fails, debug and retry (check column names, table references, syntax for the specific dialect)
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5. If results look unexpected, run sanity checks before proceeding
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**If no data warehouse is connected:**
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1. Ask the user to provide data in one of these ways:
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- Paste query results directly
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- Upload a CSV or Excel file
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- Describe the schema so you can write queries for them to run
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2. If writing queries for manual execution, use the `sql-queries` skill for dialect-specific best practices
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3. Once data is provided, proceed with analysis
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### 3. Analyze
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- Calculate relevant metrics, aggregations, and comparisons
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- Identify patterns, trends, outliers, and anomalies
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- Compare across dimensions (time periods, segments, categories)
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- For complex analyses, break the problem into sub-questions and address each
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### 4. Validate Before Presenting
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Before sharing results, run through validation checks:
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- **Row count sanity**: Does the number of records make sense?
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- **Null check**: Are there unexpected nulls that could skew results?
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- **Magnitude check**: Are the numbers in a reasonable range?
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- **Trend continuity**: Do time series have unexpected gaps?
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- **Aggregation logic**: Do subtotals sum to totals correctly?
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If any check raises concerns, investigate and note caveats.
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### 5. Present Findings
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**For quick answers:**
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- State the answer directly with relevant context
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- Include the query used (collapsed or in a code block) for reproducibility
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**For full analyses:**
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- Lead with the key finding or insight
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- Support with data tables and/or visualizations
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- Note methodology and any caveats
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- Suggest follow-up questions
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**For formal reports:**
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- Executive summary with key takeaways
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- Methodology section explaining approach and data sources
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- Detailed findings with supporting evidence
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- Caveats, limitations, and data quality notes
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- Recommendations and suggested next steps
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### 6. Visualize Where Helpful
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When a chart would communicate results more effectively than a table:
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- Use the `data-visualization` skill to select the right chart type
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- Generate a Python visualization or build it into an HTML dashboard
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- Follow visualization best practices for clarity and accuracy
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## Examples
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**Quick answer:**
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```
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/analyze How many new users signed up in December?
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```
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**Full analysis:**
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```
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/analyze What's causing the increase in support ticket volume over the past 3 months? Break down by category and priority.
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```
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**Formal report:**
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```
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/analyze Prepare a data quality assessment of our customer table -- completeness, consistency, and any issues we should address.
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```
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## Tips
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- Be specific about time ranges, segments, or metrics when possible
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- If you know the table names, mention them to speed up the process
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- For complex questions, Claude may break them into multiple queries
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- Results are always validated before presentation -- if something looks off, Claude will flag it
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