Deep Agents is an extensible agent harness that provides an out-of-the-box agent for long, multi-step tasks, with features such as planning, sub-agents, filesystem access, context management, memory, and human approval of tool calls. It is used by developers building agents with different language models, and its catalogue entries extend the harness with reusable skills, MCP servers, and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/langchain-ai/deepagents/schema-explorationnpx skills add langchain-ai/deepagents --skill schema-explorationgit clone --depth 1 https://github.com/langchain-ai/deepagentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/langchain-ai/deepagents/schema-exploration)<a href="https://agentmods.dev/skills/langchain-ai/deepagents/schema-exploration"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/schema-exploration.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00057 | $0.00929 |
| Opus 5 | $0.00028 | $0.00464 |
| Sonnet 5 | $0.00011 | $0.00186 |
| Haiku 4.5 | $0.00006 | $0.00093 |
Grade A, and why
schema-exploration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 133 lines · 57 tokens per session scan A 8560156deeac
schema-exploration is a skill published in the GitHub repository langchain-ai/deepagents (28,997 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 929 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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