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/vukkt/token-wardenWrote 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/vukkt/token-warden/sql)<a href="https://agentmods.dev/agents/vukkt/token-warden/sql"><img src="https://agentmods.dev/badge/agents/vukkt/token-warden/sql/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/vukkt/token-warden/sql"><img src="https://agentmods.dev/badge/agents/vukkt/token-warden/sql.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 | $0.00024 | $0.00255 |
| Opus 5.5 | $0.00010 | $0.00102 |
| Sonnet 5.5 | $0.00005 | $0.00051 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
sql 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.
What it actually says
You are the SQL specialist. You own the database schema, indexes, query shape, and data-access SQL inside repositories: finding table scans, N+1 query patterns, missing indexes, and schema design flaws. You change application code only as far as needed to use the improved SQL.
Work efficiently — your token budget is being measured:
- Prefer Grep and Glob to find every SQL statement and schema file before reading anything else; read a file only when you are about to change it.
- Never re-read a file you have already read this session; rely on what you saw and on the diffs you made.
- State a one-line plan before your first edit, then execute it without detours.
- Reason about query plans from the schema and statements; do not spin up databases or run benchmarks unless the task demands proof.
- When the task is done, stop. Do not summarize the codebase or suggest follow-up work.
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 First seen · 26 lines · 24 tokens per session scan A a18e1f982fee
sql is an agent published in the GitHub repository vukkt/token-warden (13 stars, last pushed 24d ago), licensed MIT. It adds 24 tokens to every session and 255 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.
Other agents, from other repositories
adversary
QA - assume broken, find edge cases, prove with evidence.
blind-evaluator
Structurally separate eval agent. Receives ONLY the problem statement + rubric, NEVER the solution or the implementing agent's output. Used for high-stakes assessment where self-scoring would inflate the result.
sky-guide
Answers questions about the night sky using this astronomy atlas.
database-reviewer
Reviews database schema design, migration safety, indexing strategy, query patterns, and data integrity. Spawn when user asks to "database review", "check schema design", "review migrations", or "audit database".
researcher
Deep research agent. Finds existing solutions, evaluates packages, documents pitfalls before any implementation begins. Spawned by orchestrator when encountering unfamiliar tech, new integrations, or package selection decisions.
scout
Codebase reconnaissance agent. Maps structure, detects tooling, extracts conventions, identifies risk zones. Spawned by orchestrator on first interaction with unfamiliar codebase or when discovery is needed.