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/code2rich/agentwaker-xiaohongshu-operator/visual-generationnpx skills add code2rich/agentwaker-xiaohongshu-operator --skill visual-generationgit clone --depth 1 https://github.com/code2rich/agentwaker-xiaohongshu-operatorWrote 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/code2rich/agentwaker-xiaohongshu-operator/visual-generation)<a href="https://agentmods.dev/skills/code2rich/agentwaker-xiaohongshu-operator/visual-generation"><img src="https://agentmods.dev/badge/skills/code2rich/agentwaker-xiaohongshu-operator/visual-generation.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 | $0.00033 | $0.00213 |
| Opus 5 | $0.00016 | $0.00106 |
| Sonnet 5 | $0.00007 | $0.00043 |
| Haiku 4.5 | $0.00003 | $0.00021 |
Grade A, and why
visual-generation 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 3d 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.
What it actually says
Visual Generation
Contract
Accept a request conforming to schemas/visual-request.schema.json. Produce actual local assets and a manifest conforming to schemas/visual-manifest.schema.json; a prompt or brief alone is not a completed result.
Routing
Select the smallest suitable adapter among generated raster images, evidence screenshots, diagrams, and charts. Preserve source data, prompts or render inputs, dimensions, hashes, and inspection results.
Boundaries
This capability may write local artifacts but does not upload, publish, mutate an account, choose a platform-specific editorial strategy, or declare a consuming workflow complete. The role wrapper owns those decisions and approvals.
Failure
Report missing adapters, failed generation, unreadable text, unsupported claims, and absent evidence. Do not replace a required real asset with a placeholder or mark an uninspected asset as passed.
What ships with it
4 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.
- 3d ago First seen · 23 lines · 33 tokens per session scan A d43966799897
visual-generation is a skill published in the GitHub repository code2rich/agentwaker-xiaohongshu-operator (5 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 213 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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chengfeng-cut
剪辑中文口播原素材:逐词转录、词典修字出修字表、五轮扫描找口误与重复、汇总表与重复句子表、打开 Studio 让用户复核、复盘沉淀用户偏好与词典。只产出一份已复核的删词账本,不切媒体、不做字幕、不做分镜动画。用户说剪口播、处理口误、生成口播基础素材、继续剪口播,或确认卡回传 action=returncutreview 时使用。不要用于执行物理剪切、导出剪后视频、单独安装、单独打开工作台或口播分镜成片。.
chengfeng-check-updates
剪辑环境的唯一管理者:就绪检查(skills 是否最新 → Runtime 是否配套)、Skills 更新激活、Runtime 安装与体检。用户说检查更新、安装剪辑环境、装播放器、检查剪辑环境、剪辑环境就绪了吗、配置转录凭证时使用;业务 Skill(剪口播/字幕/画面/导出)第 0 步也引用本 Skill 的就绪检查。不用于剪辑、字幕、画面、导出本身或项目数据迁移。.
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