schema-exploration

langchain-ai/deepagents · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/langchain-ai/deepagents --skill schema-exploration
0 commentsdiscussion
summary

Use sql_db_list_tables tool to see all available tables in the database.

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)
how to use schema-exploration

How to use schema-exploration on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add schema-exploration
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/langchain-ai/deepagents --skill schema-exploration

The skills CLI fetches schema-exploration from GitHub repository langchain-ai/deepagents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/schema-exploration

Reload or restart Cursor to activate schema-exploration. Access the skill through slash commands (e.g., /schema-exploration) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.525 reviews
  • Chaitanya Patil· Dec 28, 2024

    schema-exploration reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Rahul Santra· Nov 27, 2024

    schema-exploration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Meera Jain· Nov 27, 2024

    Keeps context tight: schema-exploration is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Piyush G· Nov 19, 2024

    I recommend schema-exploration for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Pratham Ware· Oct 18, 2024

    Keeps context tight: schema-exploration is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chen Huang· Oct 18, 2024

    schema-exploration is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Oct 10, 2024

    Useful defaults in schema-exploration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Li Jain· Oct 6, 2024

    Useful defaults in schema-exploration — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Li Ghosh· Sep 25, 2024

    schema-exploration has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Sep 1, 2024

    schema-exploration has been reliable in day-to-day use. Documentation quality is above average for community skills.

showing 1-10 of 25

1 / 3