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/modelstudioai/openagentpack/image-to-codenpx skills add modelstudioai/OpenAgentPack --skill image-to-codegit clone --depth 1 https://github.com/modelstudioai/OpenAgentPackWhat 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 | $0.00116 | $0.07704 |
| Opus 5 | $0.00058 | $0.03852 |
| Sonnet 5 | $0.00023 | $0.01541 |
| Haiku 4.5 | $0.00012 | $0.00770 |
Grade A, and why
image-to-code 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 yesterday.
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.
This is a copy
100% identical to image-to-code — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CORE DIRECTIVE: IMAGE-FIRST WEBSITE DESIGN TO CODE
You are an elite web design art director and implementation strategist.
Your job is not to generate generic website mockups. Your job is to generate premium, artistic, implementation-friendly website section references and then turn them into real frontend.
This skill is for:
- hero sections
- landing pages
- marketing sites
- startup sites
- editorial brand pages
- product pages
- portfolio websites
- premium multi-section websites
- redesigns where visual quality matters
Standard AI output tends to collapse into repetitive defaults:
- one single giant compressed image for too many sections
- text that becomes too small to read
- centered dark hero clichés
- generic card spam
- repeated left-text/right-image layouts
- weak typography hierarchy
- vague spacing
- cards inside cards inside cards
- giant rounded section containers everywhere
- too much visible information in the first screen
- tiny pills, labels, tags, system markers, and fake interface jargon
- nice-looking but unextractable designs
- generic coded reinterpretations after the image step
- lazily generating too few images for too many sections
Your goal is to aggressively break these defaults.
The output must feel:
- premium
- art-directed
- readable
- structured
- implementation-friendly
- deeply analyzable
- visually strong
- faithful enough to build from
- clean on first view
- responsive in spirit
- realistic on a small laptop viewport
IMPORTANT: For visual website tasks, you must first generate the design image(s) yourself. Then you must deeply analyze the generated image(s). Only after that should you implement the frontend.
Do not skip image generation when image generation is available. Do not begin with freeform coding first. The generated image(s) are the primary visual source of truth.
The required workflow is:
image generation first
deep image analysis second
implementation third
If the task is mainly visual, this order is mandatory.
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.
- yesterday First seen · 1,229 lines · 116 tokens per session scan A 4c060a8064a8
image-to-code is a skill published in the GitHub repository modelstudioai/OpenAgentPack (23 stars, last pushed 5d ago), licensed Apache-2.0. It adds 116 tokens to every session and 7,704 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to image-to-code, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
agent-reach
MUST USE when user wants to 调研/research/搜索/search/查/找/look up anything on the internet — e.g. 全网调研 X / 帮我调研一下 X / 查一下 X / 搜搜 X / 看看大家怎么评价 X / X 上有什么讨论 / research this topic。 Also MUST USE when user mentions any platform or shares any URL/链接: 小红书/xiaohongshu/xhs, Twitter/推特/X, B站/bilibili, Reddit, Facebook, Instagram…
auditing-terraform-infrastructure-for-security
Auditing Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to detect overly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud deployment.
bernstein-run
Run a verified multi-agent goal with Bernstein. Use when a task is too large for a single agent session: Bernstein decomposes the goal into tasks, spawns CLI coding agents in parallel git worktrees, verifies their output, and merges results. Also use to check run status, costs, and to verify a finished run against its…
bernstein-plan
Create and manage multi-step execution plans in Bernstein. Plans decompose complex goals into stages with dependencies. Use when the user wants to plan a complex feature, break down a large task, or review an execution plan before agents start working.
bernstein-approve
Review and approve/reject pending tasks or plans in Bernstein. Use when the user asks about approvals, wants to review agent work, or needs to approve/reject a plan before execution begins.
bernstein-quality
Show quality metrics for Bernstein runs - success rates per model, lint/test pass rates, completion time distributions. Use when the user asks about quality, reliability, which model performs best, or pass rates.