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/glincker/claude-code-marketplace/database-querynpx skills add glincker/claude-code-marketplace --skill database-querygit clone --depth 1 https://github.com/glincker/claude-code-marketplaceWrote 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/glincker/claude-code-marketplace/database-query)<a href="https://agentmods.dev/skills/glincker/claude-code-marketplace/database-query"><img src="https://agentmods.dev/badge/skills/glincker/claude-code-marketplace/database-query.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 | $0.00019 | $0.03145 |
| Opus 5 | $0.00010 | $0.01572 |
| Sonnet 5 | $0.00004 | $0.00629 |
| Haiku 4.5 | $0.00002 | $0.00314 |
Grade A, and why
database-query 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 4d 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 — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Query (Natural Language)
⚡ UNIQUE FEATURE: Query any database using natural language - automatically generates optimized SQL/NoSQL queries, explains query plans, suggests indexes, and visualizes results. Supports PostgreSQL, MySQL, MongoDB, SQLite, and more.
What This Skill Does
Transform natural language into optimized database queries:
- Natural language to SQL: "Show me users who signed up last month" →
SELECT * FROM users WHERE created_at >= NOW() - INTERVAL '1 month' - Multi-database support: PostgreSQL, MySQL, MongoDB, SQLite, Redis
- Query optimization: Analyzes queries and suggests improvements
- Index suggestions: Recommends indexes for slow queries
- Visual results: Formats query results as tables, charts, JSON
- Query explanation: EXPLAIN ANALYZE with human-readable insights
- Safe mode: Read-only by default with confirmation for writes
- Schema discovery: Auto-learns database structure
Why This Is Unique
First Claude Code skill that:
- Understands intent: Translates vague requests to precise queries
- Cross-database compatible: Same natural language works across SQL/NoSQL
- Performance-aware: Automatically optimizes and suggests indexes
- Safety-first: Prevents destructive operations without confirmation
- Learning mode: Improves by understanding your schema
Instructions
Phase 1: Database Connection & Discovery
-
Identify Database:
Ask user: - Database type (PostgreSQL, MySQL, MongoDB, SQLite, etc.) - Connection method (local, remote, Docker, MCP server) - Connection string or credentials -
Test Connection:
# PostgreSQL psql -h localhost -U user -d database -c "SELECT version();" # MySQL mysql -h localhost -u user -p database -e "SELECT VERSION();" # MongoDB mongosh "mongodb://localhost:27017/database" --eval "db.version()" # SQLite sqlite3 database.db "SELECT sqlite_version();" -
Discover Schema:
# PostgreSQL: Get all tables and columns psql -d database -c "\dt" psql -d database -c "\d+ table_name" # MySQL: Show database structure mysql database -e "SHOW TABLES;" mysql database -e "DESCRIBE table_name;" # MongoDB: List collections and sample documents mongosh database --eval "db.getCollectionNames()" mongosh database --eval "db.collection.findOne()"
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 475 lines · 19 tokens per session scan A 2a4502c1b901
database-query is a skill published in the GitHub repository glincker/claude-code-marketplace (36 stars, last pushed 9mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 3,145 once invoked, about $0.0001 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.
Other skills, from other repositories
database-query
Generate, optimize, and explain SQL queries - supports SQLite, PostgreSQL, MySQL with schema introspection, migration generation, and query performance analysis.
graphjin-eval
Create, extend, run, baseline, and diagnose GraphJin agent evaluations through the graphjin eval CLI.
graphjin-env
Use when setting up a training or evaluation loop against a GraphJin agent environment — running the container, reading /health, driving episodes hosted or step-by-step or with your own agent over MCP, splitting train from eval, exporting trajectories, and deciding whether two rewards can be compared.
graphjin-env-workflows
Use when running GraphJin's own environment and evaluation workflows in this repository — generating a suite, cloning or minting a world, authoring tasks, serving graded episodes, sampling, exporting trajectories, publishing a benchmark run — or when changing code those workflows depend on.
add-graphjin-database
Use when adding a new GraphJin database, warehouse, or CQL/NoSQL backend; building a simulator because no live service is available; wiring a dialect, discovery, tests, scripts, README/CONFIG/FEATURES, or website database support surfaces.
Database Schema Reviewer
Reviews database schemas for normalization issues, missing indexes, naming inconsistencies, and scalability risks.