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 rianvdm/product-ai-public --skill visual-eli5git clone --depth 1 https://github.com/rianvdm/product-ai-publicWrote 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/rianvdm/product-ai-public/visual-eli5)<a href="https://agentmods.dev/skills/rianvdm/product-ai-public/visual-eli5"><img src="https://agentmods.dev/badge/skills/rianvdm/product-ai-public/visual-eli5/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/rianvdm/product-ai-public/visual-eli5"><img src="https://agentmods.dev/badge/skills/rianvdm/product-ai-public/visual-eli5.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 | $0.00056 | $0.03658 |
| Opus 5.5 | $0.00022 | $0.01463 |
| Sonnet 5.5 | $0.00011 | $0.00732 |
| Haiku 4.5 | $0.00006 | $0.00366 |
Grade A, and why
visual-eli5 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.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual ELI5
Boundaries
The outcome is that a novice can say the one or two important things in plain language after seeing the page. Route the request before building:
- Therapy-site work goes to
building-psych-explainers. - Reader-controlled or durable internal explainers go to
interactive-explainer. - An editable whiteboard goes to
building-tldraw-diagrams. - Prose only uses a normal response or
linear-walkthrough.
An explicit $visual-eli5 controls this temporary explanatory build. One-off pages publish to whatevenis.elezea.com only when the user requests publication. The repo is ~/git/whatevenis (Worker name whateven), an assets-only Worker with a home page and one directory per subject.
Publish an approved page
Treat routine publication as a self-contained continuation of this workflow. Use the repo's local instructions, configuration, and installed CLI help; web research belongs to subject verification during authoring. Retrieve deployment documentation only when an unfamiliar command, changed configuration, or unresolved error requires it.
- Read the destination repo's instructions and check its Git state. Copy the approved page and
og.pngintopublic/<slug>/, and the share-card source intoog/<slug>.html. - Add the home-page link and README table entry. Confirm the canonical,
og:url, andog:imageURLs use the same slug. Rerun this skill's checks against the destination page. - Check the newest Worker version's
metadata.sourcebefore deploying; reconcile any API-only changes. Runnpx wrangler deploy --dry-run, thennpx wrangler deployfrom the repo root. - Confirm the new version is active through the deployments list. Verify the page and home link in a browser wider than the page column. Bot Fight Mode on elezea.com can return 403 to shell requests; use the browser for live-page inspection.
For another destination or hosting changes, use that destination's deployment workflow.
Output contract
Set page_dir="$(mktemp -d)" and create exactly one self-contained "$page_dir/index.html", plus the share card pair "$page_dir/og-card.html" and "$page_dir/og.png"; keep all review evidence in "$page_dir/review". Never create a repository or edit/deploy a destination without a separate request and destination workflow. Open the local page for inspection, and do not deploy it. The page uses its subject's objects, relationships, scale, or sequence as the composition; every picture earns its place by demonstrating a claim.
What ships with it
3 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.
- 1mo ago First seen · 107 lines · 56 tokens per session scan A 46be8c395d31
visual-eli5 is a skill published in the GitHub repository rianvdm/product-ai-public (18 stars, last pushed 21d ago), licensed MIT. It adds 56 tokens to every session and 3,658 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 2026-09-09.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
exam-forecast
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.