data-analysis-workflows

SKILL.md 节选

# Data Analysis Workflows

Complete workflows for common data analysis tasks. Covers the full process from understanding requirements to delivering validated results.

## Workflow 1: Answer Data Questions

Answer a data question, from a quick lookup to a full analysis to a formal report.

### 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

**Options for data access:**

1. Ask the user to provide data:
   - Paste query results directly
   - Upload a CSV or Excel file
   - Describe the schema so you can write queries for them to run
2. If writing queries for manual execution, use the \`sql-queries\` skill for dialect-specific best practices
3. 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 findings
- Methodology section explaining approach and data sources
- Detailed findings with supporting evidence
- Caveats and limitations
- Recommendations and next steps
- Appendix with full queries and data tables

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## Workflow 2: Explore and Profile Datasets

Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.

### 1. Access the Data

**If a file is provided (CSV, Excel, Parquet, JSON):**
1. Read the file and load into a working dataset
2. If the file is large, sample it for profiling

**If user describes a table:**
1. Ask for sample data or schema information
2. Generate profiling queries for the user to run

### 2. Profile the Dataset

Generate a report covering:

#### Basic Statistics
- **Row count**: Total number of records
- **Column count**: Number of fields
- **Primary key**: Identify unique identifier(s)
- **Grain**: One row per what? (user, order, event, etc.)
- **Time range**: For temporal data, first and last dates

#### Column-Level Analysis

For each column, report:
- **Type**: String, integer, float, date, boolean, etc.
- **Null rate**: Percentage of missing values
- **Cardinality**: Number of unique values
- **Sample values**: Examples of what the column contains
- **Distribution**: For numeric columns, min/max/mean/median/std dev

#### Data Quality Flags

Automatically identify potential issues:
- High null rates (>10%)
- Unexpected values (e.g., negative values in a count field)
- Duplicate rows
- Inconsistent formatting (mixed case, trailing spaces)

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