Explore a table or data file before starting analysis. Summarize its structure, missing values, duplicates, and distributions, flag potential quality issues, and suggest useful measures, grouping columns, and follow-up analyses.
中文简介正在准备中。当前显示英语简介。
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
安装前可以查看交给 AI 代理的指令内容。
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.
/explore-data <table_name or file>
If a data warehouse MCP server is connected:
If a file is provided (CSV, Excel, Parquet, JSON):
If neither:
Before analyzing any data, understand its structure:
Table-level questions:
Column classification — categorize each column as one of:
Run the following profiling checks:
Table-level metrics:
All columns:
Numeric columns (metrics):
min, max, mean, median (p50)
standard deviation
percentiles: p1, p5, p25, p75, p95, p99
zero count
negative count (if unexpected)
String columns (dimensions, text):
min length, max length, avg length
empty string count
pattern analysis (do values follow a format?)
case consistency (all upper, all lower, mixed?)
leading/trailing whitespace count
Date/timestamp columns:
min date, max date
null dates
future dates (if unexpected)
distribution by month/week
gaps in time series
Boolean columns:
true count, false count, null count
true rate
Present the profile as a clean summary table, grouped by column type (dimensions, metrics, dates, IDs).
Apply the quality assessment framework below. Flag potential problems:
After profiling individual columns:
Based on the column profile, recommend:
Suggest 3-5 specific analyses the user could run next:
## Data Profile: [table_name]
### Overview
- Rows: 2,340,891
- Columns: 23 (8 dimensions, 6 metrics, 4 dates, 5 IDs)
- Date range: 2021-03-15 to 2024-01-22
### Column Details
[summary table]
### Data Quality Issues
[flagged issues with severity]
### Recommended Explorations
[numbered list of suggested follow-up analyses]
Rate each column:
Look for:
Red flags that suggest accuracy issues:
For numeric columns, characterize the distribution:
For time series data, look for:
Identify natural segments by:
Between numeric columns:
When documenting a dataset for team use:
## Table: [schema.table_name]
**Description**: [What this table represents]
**Grain**: [One row per...]
**Primary Key**: [column(s)]
**Row Count**: [approximate, with date]
**Update Frequency**: [real-time / hourly / daily / weekly]
**Owner**: [team or person responsible]
### Key Columns
| Column | Type | Description | Example Values | Notes |
|--------|------|-------------|----------------|-------|
| user_id | STRING | Unique user identifier | "usr_abc123" | FK to users.id |
| event_type | STRING | Type of event | "click", "view", "purchase" | 15 distinct values |
| revenue | DECIMAL | Transaction revenue in USD | 29.99, 149.00 | Null for non-purchase events |
| created_at | TIMESTAMP | When the event occurred | 2024-01-15 14:23:01 | Partitioned on this column |
### Relationships
- Joins to `users` on `user_id`
- Joins to `products` on `product_id`
- Parent of `event_details` (1:many on event_id)
### Known Issues
- [List any known data quality issues]
- [Note any gotchas for analysts]
### Common Query Patterns
- [Typical use cases for this table]
When connected to a data warehouse, use these patterns to discover schema:
-- List all tables in a schema (PostgreSQL)
SELECT table_name, table_type
FROM information_schema.tables
WHERE table_schema = 'public'
ORDER BY table_name;
-- Column details (PostgreSQL)
SELECT column_name, data_type, is_nullable, column_default
FROM information_schema.columns
WHERE table_name = 'my_table'
ORDER BY ordinal_position;
-- Table sizes (PostgreSQL)
SELECT relname, pg_size_pretty(pg_total_relation_size(relid))
FROM pg_catalog.pg_statio_user_tables
ORDER BY pg_total_relation_size(relid) DESC;
-- Row counts for all tables (general pattern)
-- Run per-table: SELECT COUNT(*) FROM table_name
When exploring an unfamiliar data environment:
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Review a design or web page against WCAG 2.1 AA, an accessibility standard. Organize findings on contrast, keyboard access, forms, and screen reader behavior, with severity levels and practical recommendations for fixes.
中文简介正在准备中。当前显示英语简介。
Build a single brief on an existing customer using available sales records, emails, documents, call notes, and internal chat. Understand the current situation, key contacts, recent discussions, and gaps that may need attention.
中文简介正在准备中。当前显示英语简介。
Build or refresh a strategic customer account plan from sales records, emails, and meeting transcripts. Organize goals, stakeholders, growth opportunities, risks, and dated actions into a document, then sync approved highlights to the customer relationship management system.
中文简介正在准备中。当前显示英语简介。
Research prospective companies and contacts, check existing customer records, and assess fit against your ideal customer profile. Receive a sourced brief covering recent developments, likely priorities, outreach talking points, and a suggested next step.
中文简介正在准备中。当前显示英语简介。
Research sales accounts using Common Room data to summarize company details, buying signals, ownership, and key contacts. Answer specific questions directly or prepare a broader briefing with evidence-based next steps, clearly identifying missing information.
中文简介正在准备中。当前显示英语简介。
Score and group accounts by customer fit and engagement, such as recent contact and open sales opportunities. Identify where to focus sales effort, choose next actions, and spot promising accounts with neglected outreach or missing information.
中文简介正在准备中。当前显示英语简介。