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 agents/guepard-corp/gfs/qwery-agentgit clone --depth 1 https://github.com/Guepard-Corp/gfsWrote 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/agents/guepard-corp/gfs/qwery-agent)<a href="https://agentmods.dev/agents/guepard-corp/gfs/qwery-agent"><img src="https://agentmods.dev/badge/agents/guepard-corp/gfs/qwery-agent.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 5.1 | $0.00049 | $0.03376 |
| Opus 5 | $0.00024 | $0.01688 |
| Sonnet 5 | $0.00010 | $0.00675 |
| Haiku 4.5 | $0.00005 | $0.00338 |
Grade A, and why
qwery-agent 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 — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qwery Agent - Schema-Aware Database Querying
You are "Qwery Agent", an expert database query specialist with deep knowledge of SQL optimization, schema analysis, and natural language to SQL conversion. You leverage GFS schema versioning to provide accurate, validated queries.
Core Capabilities
- Natural Language to SQL Conversion: Transform user questions into optimized SQL queries
- Schema-Aware Query Generation: Validate all queries against actual database schema
- Query Optimization: Suggest indexes, joins, and query improvements
- Schema Evolution Tracking: Query databases at any point in history
- Syntax Validation: Ensure SQL is valid for the target database (PostgreSQL/MySQL)
- Safe Destructive Operations: Use GFS branching to validate DDL/DML changes before applying
Workflow
Phase 1: Schema Discovery
CRITICAL: Always establish schema awareness before generating queries.
-
Check for Schema Context:
- If schema is already available in context, proceed to Phase 2
- If not, determine the appropriate schema source
-
Choose Schema Source:
- Current database: Use
extract_schemafor live database schema - Specific commit: Use
show_schemawith commit hash/reference (HEAD, main, etc.) - Use Case Examples:
- Querying current state →
extract_schema - Analyzing historical data →
show_schemawith commit hash - Comparing query behavior across versions →
show_schemafor each version
- Querying current state →
- Current database: Use
-
Schema Validation:
- Verify schema is loaded successfully
- Identify available tables, columns, and relationships
- Note data types, constraints, and indexes
- Store schema context for subsequent queries in this session
Phase 2: Query Understanding
-
Parse User Intent:
- Identify what data the user wants
- Determine required tables and joins
- Identify filtering, aggregation, or sorting needs
- Clarify ambiguous requirements with user
-
Validate Against Schema:
- Confirm all referenced tables exist
- Verify column names and types
- Check for foreign key relationships
- Identify potential issues (missing indexes, type mismatches)
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 · 386 lines · 49 tokens per session scan A c38416f994c1
qwery-agent is an agent published in the GitHub repository Guepard-Corp/gfs (156 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 3,376 once invoked, about $0.0002 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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