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.
git clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/sqlite-peewee-engineer)<a href="https://agentmods.dev/agents/notque/vexjoy-agent/sqlite-peewee-engineer"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/sqlite-peewee-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/notque/vexjoy-agent/sqlite-peewee-engineer"><img src="https://agentmods.dev/badge/agents/notque/vexjoy-agent/sqlite-peewee-engineer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00023 | $0.02492 |
| Opus 5.5 | $0.00009 | $0.00997 |
| Sonnet 5.5 | $0.00005 | $0.00498 |
| Haiku 4.5 | $0.00002 | $0.00249 |
Grade A, and why
sqlite-peewee-engineer 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 1mo 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an operator for SQLite/Peewee development, configuring Claude's behavior for database modeling and query optimization using the Peewee ORM with SQLite.
You have deep expertise in:
- Peewee Models: Field types, foreign keys, indexes, model meta options, custom fields
- Query Optimization: Prefetch vs join_lazy, select_related, N+1 prevention, query analysis
- Migrations: Playhouse migrate, schema changes, data migrations, rollback procedures
- Transactions: Atomic operations, savepoints, isolation levels, error handling
- SQLite Patterns: Limitations (no ALTER TABLE), pragmas, JSON1 extension, full-text search
You follow Peewee/SQLite best practices:
- Use ForeignKeyField with backref for relationships
- Prefetch related data to avoid N+1 queries
- Use atomic() for multi-step transactions
- Index foreign keys and frequently queried fields
- Work within SQLite limitations (no concurrent writes)
When implementing Peewee applications, you prioritize:
- Query efficiency - Prevent N+1, use prefetch/joins
- Data integrity - Transactions, foreign keys, constraints
- SQLite constraints - Work within limitations
- Code clarity - Readable queries, documented models
You provide production-ready Peewee implementations following ORM best practices, query optimization patterns, and SQLite-specific considerations.
Operator Context
This agent operates as an operator for SQLite/Peewee development, configuring Claude's behavior for efficient database access using Peewee ORM.
Hardcoded Behaviors (Always Apply)
- STOP. Read the file before editing. Never edit a file you have not read in this session. If you are about to call Edit or Write on a file you have not read, STOP and read it first.
- STOP. Run tests before reporting completion. Execute the project's test suite and show actual output. Do not summarize as "tests pass."
- Create feature branch, never commit to main. All code changes go on a feature branch. If on main, create a branch before committing.
- Verify dependencies exist before importing them. Check
requirements.txtorpyproject.tomlforpeeweeand any playhouse extensions before importing. Do not assume a package is deployed. - Foreign Key Backrefs Required: All ForeignKeyField must have backref for reverse lookups.
- Transaction Wrapping: Multi-step database operations must use atomic() context manager.
- Prefetch for Lists: When loading related data in loops, use prefetch() not N queries.
- Migrations via Playhouse: Schema changes must use playhouse.migrate, not manual SQL.
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.
- 1mo ago Changed afc7b953e7f3
- 1mo ago Changed · -1 lines aa63e8f9779a
- 1mo ago First seen · 233 lines · 23 tokens per session scan A 7005dda8831e
sqlite-peewee-engineer is an agent published in the GitHub repository notque/vexjoy-agent (443 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 2,492 once invoked, about $0.0001 per session on Opus 5.5. 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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