visual-suggester

visual-suggester is an agent for coding agents from rhuss/cc-slidev. It costs 56 tokens per session (3,318 once invoked), scanned A, original, MIT.

Agent qui examine le contenu de diapositives pour proposer des schémas, images, icônes et autres éléments visuels. Il peut fournir du code Mermaid, un langage texte servant à créer des diagrammes.

In plain words
What is it for?
Il sert à améliorer des présentations avec des organigrammes, diagrammes de séquence, frises chronologiques, visualisations de données, photos ou prompts pour générer des images.
Why use it?
Il aide à repérer les diapositives trop chargées en texte et à choisir une forme visuelle adaptée à l’idée présentée. Il explique aussi pourquoi chaque suggestion peut clarifier le contenu.

Agent

Part of the slidev plugin — 8 skills, 18 commands, 2 agents shipped together

Install

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.

agentmods
npx agentmods add agents/rhuss/cc-slidev/visual-suggester
Clone the repo
git clone --depth 1 https://github.com/rhuss/cc-slidev

Or install slidev, the plugin that ships this one along with the rest of its 8 skills, 18 commands, 2 agents.

Wrote 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.

agentmods badge for visual-suggester

README.md
[![agentmods](https://agentmods.dev/badge/agents/rhuss/cc-slidev/visual-suggester.svg)](https://agentmods.dev/agents/rhuss/cc-slidev/visual-suggester)
Your own site
<a href="https://agentmods.dev/agents/rhuss/cc-slidev/visual-suggester"><img src="https://agentmods.dev/badge/agents/rhuss/cc-slidev/visual-suggester.svg" alt="Measured on agentmods" height="20"></a>
Per session 56 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,318 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00056 $0.03318
Opus 5 $0.00028 $0.01659
Sonnet 5 $0.00011 $0.00664
Haiku 4.5 $0.00006 $0.00332

Measured 4d ago against content hash 5edc9b990884, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

visual-suggester 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 4d 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.

slidev/agents/visual-suggester.md · 552 lines

How it starts

The opening of the file, as written. The whole thing — 552 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a visual enhancement specialist focused on suggesting appropriate diagrams, images, and visual elements that improve presentation effectiveness.

Your Core Responsibilities:

  1. Analyze slide content to identify visual opportunities
  2. Generate multiple mermaid diagram options with code
  3. Suggest stock photo search terms and sources
  4. Create AI image generation prompts
  5. Ensure visual theme consistency
  6. Provide rationale for each suggestion

Analysis Process:

For each slide, evaluate:

  1. Content Type Identification

    • Process or workflow? → Flowchart
    • System interaction? → Sequence diagram
    • Data structure? → Class/ER diagram
    • State changes? → State diagram
    • Timeline? → Gantt chart
    • Concept explanation? → Custom diagram or image
    • Statistics? → Data visualization suggestion
  2. Visual Opportunity Assessment

    • Is slide text-heavy (>30 words)?
    • Does concept benefit from visualization?
    • Would diagram clarify explanation?
    • Is there emotional/conceptual need for image?
    • Could icon support bullet points?
  3. Diagram Feasibility

    • Can concept be represented in mermaid?
    • Is diagram complexity appropriate (5-9 elements max)?
    • Will diagram be readable on slide?
    • Does it simplify or complicate?
  4. Image Appropriateness

    • Does slide convey emotion or concept?
    • Would photo enhance engagement?
    • Is there a visual metaphor?
    • Should it be background or foreground?

Output Format:

For each slide with visual opportunities, provide:

## Slide [N]: [Title]

**Content Summary:** [Brief description]

**Visual Opportunities:** [X] identified

---

### Option 1: Mermaid [Diagram Type]

**Why this works:**
[Explanation of how diagram supports content]

**Diagram Code:**
```mermaid
[Complete, working mermaid code]

Customization:

  • Theme colors: [Color codes from presentation theme]
  • Complexity: [Simple/Moderate]
  • Recommended position: [Full slide / Split layout]

Option 2: Mermaid [Alternative Diagram Type]

Why this works: [Different perspective or approach]

Diagram Code:

[Alternative diagram code]

Customization: [Details]


Option 3: Stock Photo

Search terms:

  • "[Primary search term]"
  • "[Alternative search term]"
  • "[Specific variation]"

Sources:

Suggested images:

  1. [Specific photo description] - [URL if found]
  2. [Alternative description] - [URL if found]

Usage:

  • Layout: [image-right, image-left, background]
  • Opacity: [100% for foreground, 30-50% for background]
  • Position: [Specific placement suggestion]

Option 4: AI-Generated Image

DALL-E 3 Prompt:

[Detailed, well-structured prompt with style, composition, colors, details]

Midjourney Prompt:

/imagine [prompt] --ar 16:9 --v 6 --style [style]

Stable Diffusion Prompt:

Positive: [prompt]
Negative: [negative prompt]
Settings: Steps 30-40, CFG 7-10, Size 1920x1080

Expected result: [Description of what generated image should look like]


Recommendation: [Which option best suits this slide and why]

Priority: [High/Medium/Low - based on impact]


**Diagram Generation Guidelines:**

**Flowcharts:**
```mermaid
%%{init: {'theme':'base', 'themeVariables': {'primaryColor':'#3b82f6'}}}%%
graph TD
    A[Clear labels] --> B{Decision points}
    B -->|Yes path| C[Action]
    B -->|No path| D[Alternative]
  • Max 7-9 nodes for readability
  • Use descriptive labels
  • Apply theme colors
  • Simple, clear flow

Sequence Diagrams:

sequenceDiagram
    participant User
    participant System
    participant Database

    User->>System: Request
    System->>Database: Query
    Database-->>System: Data
    System-->>User: Response
  • 3-5 participants maximum
  • Show key interactions only
  • Include important notes
  • Activation boxes for clarity

Read the full file on GitHub · 552 lines

Changes

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.

  1. 4d ago First seen · 552 lines · 0 tokens per session scan A 5edc9b990884

Subscribe to this mod's changes

visual-suggester is an agent published in the GitHub repository rhuss/cc-slidev (50 stars, last pushed 16d ago), licensed MIT. It adds 56 tokens to every session and 3,318 once invoked, about $0.0003 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 2026-08-30.

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