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 skills/biolytics-ai/rhetoric-engine/visual-reasonernpx skills add Biolytics-AI/rhetoric-engine --skill visual-reasonergit clone --depth 1 https://github.com/Biolytics-AI/rhetoric-engineWrote 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/skills/biolytics-ai/rhetoric-engine/visual-reasoner)<a href="https://agentmods.dev/skills/biolytics-ai/rhetoric-engine/visual-reasoner"><img src="https://agentmods.dev/badge/skills/biolytics-ai/rhetoric-engine/visual-reasoner.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 | $0.00041 | $0.00741 |
| Opus 5 | $0.00020 | $0.00370 |
| Sonnet 5 | $0.00008 | $0.00148 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
visual-reasoner 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Reasoner
Choose visual forms that help the audience reason from thesis to belief. Do not choose visuals before the slide thesis is clear.
Inputs
- Approved slide thesis map with audience question, answer thesis, and proof job per slide.
- Argument spine, evidence needs, and known objections.
- Available data, examples, images, diagrams, source material, and constraints.
- Cognitive design notes, accessibility requirements, and medium constraints when available.
Outputs
Return a Visual Reasoning Plan with:
visual_plan_per_slide: selected reasoning pattern, visual role, key encodings, and audience takeaway.chart_diagram_recommendations: chart, diagram, table, map, timeline, process, hierarchy, comparison, or annotated figure choices.annotation_plan: labels, callouts, emphasis, sequencing, and explanation needed to make the visual believable.rejected_visuals: decorative, weak, misleading, or thesis-mismatched visuals and why they were rejected.
Workflow
- Confirm the slide thesis, audience question, and proof job are explicit.
- Select a visual reasoning pattern that matches the thinking task: compare, sequence, locate, quantify, classify, explain causality, show structure, reveal change, or decide.
- Map evidence to visual elements only where it proves, clarifies, or qualifies the thesis.
- Recommend the simplest chart or diagram that makes the relationship legible and credible.
- Plan annotations that direct attention to the claim-relevant signal, not every available detail.
- Reject visuals that decorate, imply unsupported causality, hide uncertainty, or compete with the thesis.
- Flag slides that need stronger evidence, a clearer thesis, or cognitive design review before visual selection can proceed.
Evaluation Checks
- Every visual has a reasoning job tied to the slide thesis.
- The recommended form fits the data relationship and audience task.
- Annotations make the belief path visible.
- Decorative or weak visuals are explicitly rejected.
- Uncertainty, caveats, and comparisons are not visually distorted.
- No visual choice depends on a specific renderer or slide backend.
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 · 66 lines · 41 tokens per session scan A d1d3fead195e
visual-reasoner is a skill published in the GitHub repository Biolytics-AI/rhetoric-engine (1 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 741 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 2026-08-31.
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