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/rittmananalytics/wire-pluginWrote 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/commands/rittmananalytics/wire-plugin/dbt-audit-review)<a href="https://agentmods.dev/commands/rittmananalytics/wire-plugin/dbt-audit-review"><img src="https://agentmods.dev/badge/commands/rittmananalytics/wire-plugin/dbt-audit-review/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/commands/rittmananalytics/wire-plugin/dbt-audit-review"><img src="https://agentmods.dev/badge/commands/rittmananalytics/wire-plugin/dbt-audit-review.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 | $0.00007 | $0.02352 |
| Opus 5 | $0.00003 | $0.01176 |
| Sonnet 5 | $0.00001 | $0.00470 |
| Haiku 4.5 | $0.00001 | $0.00235 |
Grade A, and why
dbt-audit-review 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 8d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 8d ago First seen · 217 lines · 7 tokens per session scan A 22b164cfa784
dbt-audit-review is a command published in the GitHub repository rittmananalytics/wire-plugin (8 stars, last pushed 4d ago), with no licence file. It adds 7 tokens to every session and 2,352 once invoked, about $0.0000 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 2026-09-03.
Other commands, from other repositories
superpowers-status
Show the complete health of the project's AI Literacy habitat — harness enforcement, agent team, compound learning, model routing, and CI status.
optimize-pipeline
Optimize an existing pipeline on demand — survey topology, model-tier cost, and past-run signals, lock an optimization plan with the user, then batch-apply it atomically with a mandatory post-apply audit.
change-models
Set, change, or audit model preferences for pipeline agents — interactive 6-mode workflow (global/workspace prefs, per-agent overrides, first-run wizard, catalog refresh).
multi-execute
Multi-model collaborative execution: prototype from plan, refactor, multi-model audit, delivery.
optimize-prompt
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles: common words tokenize more efficiently, unusual words break into more tokens, and conciseness reduces cost.
prompt-audit
Discover and review LLM prompts in this codebase against the best-practices rubric. Reports findings and proposed diffs in the terminal — never edits without approval.