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/oozoofrog/oozoofrog-pluginsWrote 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/oozoofrog/oozoofrog-plugins/verification-scientist)<a href="https://agentmods.dev/agents/oozoofrog/oozoofrog-plugins/verification-scientist"><img src="https://agentmods.dev/badge/agents/oozoofrog/oozoofrog-plugins/verification-scientist/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/oozoofrog/oozoofrog-plugins/verification-scientist"><img src="https://agentmods.dev/badge/agents/oozoofrog/oozoofrog-plugins/verification-scientist.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 | $0.00046 | $0.02146 |
| Opus 5.5 | $0.00018 | $0.00858 |
| Sonnet 5.5 | $0.00009 | $0.00429 |
| Haiku 4.5 | $0.00005 | $0.00215 |
Grade A, and why
verification-scientist 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 202 lines · 46 tokens per session scan A c89f667327b2
verification-scientist is an agent published in the GitHub repository oozoofrog/oozoofrog-plugins (2 stars, last pushed 4mo ago), with no licence file. It adds 46 tokens to every session and 2,146 once invoked, about $0.0002 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 2026-08-31.
Other agents, from other repositories
figure-accessibility-and-style-reviewer
Audits PaperLab figures for readability, accessibility, technical style, print safety, and consistency across chapters.
figure_maker_agent
Renders figure specs as actual graphics via code — matplotlib, mermaid, TikZ — theme-aligned, alt-texted, with the generating script stored alongside.
figure-reviewer
Use this agent to review figures for publication quality. Checks semantic color usage, dimensionality representation, mathematical notation, grid alignment, and adherence to petarv/janosh principles. Use after creating or modifying diagrams.
ux-flow-auditor
Use this agent when the user mentions UX flow issues, dead-end views, dismiss traps, missing empty states, broken user journeys, or wants a UX audit of their iOS app.
ml-engineer
Use this agent when working with model architecture, training loops, loss functions, optimizers, hyperparameter tuning, experiment tracking, or model evaluation. For example: building a PyTorch model, writing a training loop with mixed precision, setting up an Optuna hyperparameter sweep, configuring MLflow experiment…
experiment-planner
An agent for planning experiments with a work breakdown structure, which divides a project into smaller tasks. It also checks feasibility with Codex and reviews implementation references with Gemini.