Agent skill
schema-exploration
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.
Install this agent skill to your Project
npx add-skill https://github.com/langchain-ai/deepagents/tree/main/examples/text-to-sql-agent/skills/schema-exploration
SKILL.md
Schema Exploration Skill
Workflow
1. List All Tables
Use sql_db_list_tables tool to see all available tables in the database.
This returns the complete list of tables you can query.
2. Get Schema for Specific Tables
Use sql_db_schema tool with table names to examine:
- Column names - What fields are available
- Data types - INTEGER, TEXT, DATETIME, etc.
- Sample data - 3 example rows to understand content
- Primary keys - Unique identifiers for rows
- Foreign keys - Relationships to other tables
3. Map Relationships
Identify how tables connect:
- Look for columns ending in "Id" (e.g., CustomerId, ArtistId)
- Foreign keys link to primary keys in other tables
- Document parent-child relationships
4. Answer the Question
Provide clear information about:
- Available tables and their purpose
- Column names and what they contain
- How tables relate to each other
- Sample data to illustrate content
Example: "What tables are available?"
Step 1: Use sql_db_list_tables
Response:
The Chinook database has 11 tables:
1. Artist - Music artists
2. Album - Music albums
3. Track - Individual songs
4. Genre - Music genres
5. MediaType - File formats (MP3, AAC, etc.)
6. Playlist - User-created playlists
7. PlaylistTrack - Tracks in playlists
8. Customer - Store customers
9. Employee - Store employees
10. Invoice - Customer purchases
11. InvoiceLine - Individual items in invoices
Example: "What columns does the Customer table have?"
Step 1: Use sql_db_schema with table name "Customer"
Response:
The Customer table has these columns:
- CustomerId (INTEGER) - Primary key, unique customer ID
- FirstName (NVARCHAR) - Customer's first name
- LastName (NVARCHAR) - Customer's last name
- Company (NVARCHAR) - Company name (optional)
- Address, City, State, Country, PostalCode - Location fields
- Phone (NVARCHAR) - Phone number
- Fax (NVARCHAR) - Fax number
- Email (NVARCHAR) - Email address
- SupportRepId (INTEGER) - Foreign key to Employee table
Sample data shows customers like:
- Luís Gonçalves from Brazil
- Leonie Köhler from Germany
- François Tremblay from Canada
Example: "How do I find revenue by artist?"
Step 1: Identify tables needed
- Artist (has artist names)
- Album (links artists to tracks)
- Track (links albums to sales)
- InvoiceLine (has sales data)
- Invoice (has revenue totals)
Step 2: Map relationships
Artist (ArtistId)
↓ 1:many
Album (ArtistId, AlbumId)
↓ 1:many
Track (AlbumId, TrackId)
↓ 1:many
InvoiceLine (TrackId, UnitPrice, Quantity)
Response:
To find revenue by artist, you'll need to:
1. Start with the Artist table
2. JOIN to Album on ArtistId
3. JOIN to Track on AlbumId
4. JOIN to InvoiceLine on TrackId
5. SUM the revenue (UnitPrice * Quantity)
6. GROUP BY artist name
This requires the query-writing skill to execute.
Quality Guidelines
For "list tables" questions:
- Show all table names
- Add brief descriptions of what each contains
- Group related tables (e.g., music catalog, transactions, people)
For "describe table" questions:
- List all columns with data types
- Explain what each column contains
- Show sample data for context
- Note primary and foreign keys
- Explain relationships to other tables
For "how do I query X" questions:
- Identify required tables
- Map the JOIN path
- Explain the relationship chain
- Suggest next steps (use query-writing skill)
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
cuml-machine-learning
Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality reduction, or model training on datasets.
cudf-analytics
Use for GPU-accelerated data analysis on datasets, CSVs, or tabular data using NVIDIA cuDF. Triggers when tasks involve groupby aggregations, statistical summaries, anomaly detection, or large-scale data profiling.
data-visualization
Use for creating publication-quality charts and multi-panel analysis summaries. Triggers when tasks involve visualizing data, plotting results, creating charts, or producing visual reports from analysis output.
gpu-document-processing
Use when processing large PDFs, document collections, or bulk text extraction tasks that benefit from GPU-accelerated processing. Triggers when the user provides large documents or needs bulk document analysis.
query-writing
Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.
social-media
Drafts engaging social media posts, writes hooks, suggests hashtags, creates thread structures, and generates companion images. Use when the user asks to write a LinkedIn post, tweet, Twitter/X thread, social media caption, social post, or repurpose content for social platforms.
Didn't find tool you were looking for?