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 skills add naveedharri/benai-skills --skill generate-visualgit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/generate-visual)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/generate-visual"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/generate-visual/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/skills/naveedharri/benai-skills/generate-visual"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/generate-visual.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00164 | $0.03412 |
| Opus 5 | $0.00082 | $0.01706 |
| Sonnet 5 | $0.00033 | $0.00682 |
| Haiku 4.5 | $0.00016 | $0.00341 |
Grade A, and why
generate-visual 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Visual
On-screen video visual generator. Takes a concept and produces either a single visual or a progressive-disclosure sequence in Ben's locked brand system.
Two Modes
single
One slide, one prompt, one call. Same flow as the thumbnail generate skill except no Ben in frame by default (slides are the visual the audience sees, Ben is talking over them). The supporting visual IS the hero.
Use when the user says: "make a slide showing X," "visual for this section," "one image of the concept."
progressive-disclosure
A sequence of N visuals (typically 2 to 6) where each frame adds one new element to the previous. The story unfolds as Ben clicks through them on-screen. Background, palette, composition, and previously-shown elements stay LOCKED across the entire sequence; the only change between frame K and frame K+1 is the ADDITION of one new piece.
Use when the user says: "progressive disclosure," "step-by-step reveal," "build this up across slides," "show the layers," "frame-by-frame walkthrough," or describes a concept that naturally unfolds in stages.
Implementation: iterative image-to-image. Generate frame 1 from a prompt. For frame 2, pass frame 1 as medias[0] and prompt the model to add element X. For frame 3, pass frame 2 (the just-generated image) as medias[0] and add element Y. Continue until all N frames are generated. This locks visual continuity because each frame is literally derived from the previous one. See references/progressive-disclosure.md for the full mechanic.
Inputs
What the skill needs from the user:
- Concept — what the visual should show. ("The skills folder structure: SKILL.md plus references/ inside it")
- Mode —
singleorprogressive-disclosure. Inferred from the concept if not stated (multi-stage concepts default to progressive-disclosure). - Number of steps — only for progressive-disclosure (default 4, max 6).
- Video slug — the topic-slug of the video this visual belongs to (used in the output path:
Projects/youtube/{video-slug}/visuals/). Required. Ask once if missing. - Reference image(s) (optional but recommended, may be multiple) — past slides, sketches, screenshots, real logo PNGs, or anything that should anchor the look. All references must be READ first via the
Readtool, then passed tomedias[]. If the user mentions a reference but doesn't provide a path or attachment, ASK for the path BEFORE doing anything else. Do not guess, do not proceed, do not generate without seeing the path.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 215 lines · 164 tokens per session scan A e680966120cb
generate-visual is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 6d ago), licensed MIT. It adds 164 tokens to every session and 3,412 once invoked, about $0.0008 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 2026-09-05.
Other skills, from other repositories
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doc-html-slide
A renderer that turns presentation content into a single HTML slide deck that opens directly in a browser. It creates a 16:9 slide sequence with navigation, fullscreen viewing, printing to PDF, and speaker-note controls.
media-higgsfield-explainer
A Higgsfield workflow for making non-photorealistic narrated explainer videos. It pairs each narration line with a 10-second animated clip and joins the clips into one finished video.
media-higgsfield-identity
A Higgsfield workflow for keeping a person, character, product, or other visual reference consistent across generated images. It chooses between a trained identity model and a one-image reference method.
media-notebooklm-slide-prompt
A prompt builder that turns lecture, class, or seminar notes into instructions for NotebookLM Studio to create slides. It also creates image prompts for each slide using Nano Banana, Google's image-generation model.
media-gpt-image-2-prompt
A prompt builder for GPT-image-2, an OpenAI image-generation model. It turns a short request into six sections covering the subject, action, scene, composition, lighting, style, and text limits.