aws-wechat-article-assets

aws-wechat-article-assets is a skill for Claude Code, Codex from aiworkskills/wechat-article-skills. It costs 258 tokens per session (4,445 once invoked), scanned A, original, Apache-2.0.

A local library for storing business descriptions, product images, and reusable writing or design presets for WeChat articles. It organizes these materials by product so other article tools can reuse them.

In plain words
What is it for?
Use it to review existing product materials, save new business descriptions, add product images with notes, and import .aws preset packages into the article workspace.
Why use it?
It prevents business information and approved visual materials from being scattered across projects. It also provides controlled importing of preset packages from allowed aiworkskills.cn addresses.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to review existing product materials, save new business descriptions, add product images with notes, and import .aws preset packages into the article workspace.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aiworkskills/wechat-article-skills/aws-wechat-article-assets
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.

Any agent
npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-assets
Clone the repo
git clone --depth 1 https://github.com/aiworkskills/wechat-article-skills

Made for: Claude Code, Codex.

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 aws-wechat-article-assets

README.md
[![agentmods](https://agentmods.dev/badge/skills/aiworkskills/wechat-article-skills/aws-wechat-article-assets/github.svg)](https://agentmods.dev/skills/aiworkskills/wechat-article-skills/aws-wechat-article-assets)
Your own site
<a href="https://agentmods.dev/skills/aiworkskills/wechat-article-skills/aws-wechat-article-assets"><img src="https://agentmods.dev/badge/skills/aiworkskills/wechat-article-skills/aws-wechat-article-assets/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.

agentmods 80×15 button for aws-wechat-article-assets

Your own site · 80×15
<a href="https://agentmods.dev/skills/aiworkskills/wechat-article-skills/aws-wechat-article-assets"><img src="https://agentmods.dev/badge/skills/aiworkskills/wechat-article-skills/aws-wechat-article-assets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 258 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,445 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00258 $0.04445
Opus 5 $0.00129 $0.02223
Sonnet 5 $0.00052 $0.00889
Haiku 4.5 $0.00026 $0.00445

Measured 5d ago against content hash c53259ee1cec, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

aws-wechat-article-assets 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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/import_presets_aws.py, scripts/product_image_ingest.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/aws-wechat-article-assets/SKILL.md · 230 lines

How it starts

The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.

用户业务资料库与预设(Assets)

业务资料按产品名分目录.aws-article/products/{产品名}/ 直挂业务介绍 .mdimages/ 存配图(含同名说明 .md)。写涉及用户自身业务的文章/配图前必须先查这里;新生成的业务介绍内容应引导用户保存到这里。预设包合并到 .aws-article/presets/

套件说明 · 本 skill 属 aws-wechat-article-* 一条龙套件(共 9 个 slug,入口 aws-wechat-article-main)。跨 skill 的相对引用依赖同一 skills/ 目录,建议一并 clawhub install 全套。源码:https://github.com/aiworkskills/wechat-article-skills

能力披露(Capabilities)

本 skill 管理本地业务资料库与预设包,可选从 aiworkskills.cn 域下载 .aws 预设包(ZIP 格式)。

  • 凭证:无
  • 网络:可选 https://*.aiworkskills.cn/**/*.aws 下载预设包。白名单强制:仅 HTTPS + aiworkskills.cn 子域,非白名单会直接报错退出。调试参数 --allow-any-host 可放宽但不推荐生产使用
  • 文件读:用户指定的本地图片路径或 .aws 文件(脚本边界);AI 在引导业务介绍入库时会先 ls .aws-article/products/ 看已有产品名
  • 文件写:仓库内 .aws-article/products/{产品名}/*.md(业务介绍,AI 用 Write 工具直接落库)、.aws-article/products/{产品名}/images/*(图片 + 同名 .md,由脚本写)、.aws-article/presets/<子目录>/*(预设文件)、.aws-article/downloads/*.aws(下载缓存)、.aws-article/tmp/*(解压临时目录);导入 .aws仓库根 aws.env 会按映射表增量写入密钥字段(覆盖现有键前自动备份为 aws.env.bak.{ts},stderr 仅打印键名不打印值)
  • 归档:解压 .aws 扩展名的 ZIP 到 .aws-article/tmp/,按白名单子目录合并到 .aws-article/presets/已内置 ZIP slip 防御:逐项校验 ZIP 成员路径,拒绝含绝对路径、.. 段或解析后指向解压目录外的路径,任一违反立即退出不写入任何文件
  • shell:仅 {python} {baseDir}/scripts/product_image_ingest.pyimport_presets_aws.py{python} = 本机 Python 3 解释器,见 main SKILL 第 0 步:Windows 用 py -3 -X utf8,macOS / Linux 用 python3

所有写入限制在仓库根下的 .aws-article/ 内。

配套 skill(informational)

本 skill 是 aws-wechat-article-* 一条龙公众号套件的业务资料库与预设环节(入口 aws-wechat-article-main)。

  • 单独安装可用:业务图入库 / .aws 预设导入两个脚本都不依赖兄弟 skill,只要 .aws-article/ 目录就能工作。
  • 其他 skill 读取 .aws-article/products/{产品名}/(业务介绍 + images/)和 .aws-article/presets/ 里由本 skill 管理的资源,属套件协同;需结合写稿/配图/排版等 skill 使用。

完整 9 slug 清单见 源码仓库

能力 说明
业务介绍 .md 入库 AI 与用户对话产出业务介绍内容时,引导/响应保存到 products/{产品名}/<文件名>.md(用 Write 工具直接落库,无脚本)
业务图入库 用户业务图 → products/{产品名}/images/(脚本 --product 必填),供其它 skill 引用
预设包 .aws ZIP 包(本地文件或 https://aiworkskills.cn/**/*.aws URL)→ 合并 presets/ 子目录;config.yaml 见下

Read the full file on GitHub · 230 lines

Files

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.

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. 5d ago Changed c53259ee1cec
  2. 11d ago First seen · 230 lines · 258 tokens per session scan A a2bdaddfd546

Subscribe to this mod's changes

aws-wechat-article-assets is a skill published in the GitHub repository aiworkskills/wechat-article-skills (589 stars, last pushed today), licensed Apache-2.0. It adds 258 tokens to every session and 4,445 once invoked, about $0.0013 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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