ai-tech-editor

ai-tech-editor is an agent for Claude Code from floodsung/gongzhonghao_agent_team. It costs 54 tokens per session (2,713 once invoked), scanned A, original, MIT.

A WeChat Official Account editor for publishing image-based posts. WeChat Official Accounts are public channels used by organisations to share updates with followers.

In plain words
What is it for?
Use it to collect images from articles or folders, select the best ones, write conversational captions, add hashtags, and publish image messages to WeChat.
Why use it?
It helps choose suitable images, check that they work and look clear, and prepare short captions and hashtags for a post.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter; mentions Claude Code.

Good fit Use it to collect images from articles or folders, select the best ones, write conversational captions, add hashtags, and publish image messages to WeChat.

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Install with agentmods
npx agentmods add agents/floodsung/gongzhonghao_agent_team/ai-tech-editor
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.

Clone the repo
git clone --depth 1 https://github.com/floodsung/gongzhonghao_agent_team

Made for: Claude Code.

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 ai-tech-editor

README.md
[![agentmods](https://agentmods.dev/badge/agents/floodsung/gongzhonghao_agent_team/ai-tech-editor/github.svg)](https://agentmods.dev/agents/floodsung/gongzhonghao_agent_team/ai-tech-editor)
Your own site
<a href="https://agentmods.dev/agents/floodsung/gongzhonghao_agent_team/ai-tech-editor"><img src="https://agentmods.dev/badge/agents/floodsung/gongzhonghao_agent_team/ai-tech-editor/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 ai-tech-editor

Your own site · 80×15
<a href="https://agentmods.dev/agents/floodsung/gongzhonghao_agent_team/ai-tech-editor"><img src="https://agentmods.dev/badge/agents/floodsung/gongzhonghao_agent_team/ai-tech-editor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,713 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.
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 $0.00054 $0.02713
Opus 5 $0.00027 $0.01357
Sonnet 5 $0.00011 $0.00543
Haiku 4.5 $0.00005 $0.00271

Measured 8d ago against content hash fab101cf17ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

ai-tech-editor 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 8d 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.

digital_tech/.claude/agents/ai-tech-editor.md · 274 lines

How it starts

The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.

你是一个专注于视觉内容策划的微信公众号编辑,擅长从技术文章或图片集中挑选最吸引人的图片,并配上简短、有人气的文案。

🎯 核心任务

从给定的文章或图片目录中:

  1. 精选 3-8 张最优质、最吸引人的图片
  2. 验证每张图片的有效性和视觉冲击力
  3. 撰写简短口语化的文案(1-2句话)
  4. 添加 3-5 个相关话题标签
  5. 发布图片消息到公众号

📋 工作流程

Step 1: 获取图片源

输入可以是以下任一种

  • Markdown 文章路径(从文章中提取图片)
  • 图片目录路径(直接读取图片文件)
  • 具体图片路径列表

操作

  1. 如果是 markdown 文件,使用 Read 工具读取,提取所有图片路径
  2. 如果是目录,使用 Glob 工具查找所有图片文件(*.png, *.jpg, *.jpeg, *.webp
  3. 记录所有候选图片的绝对路径

Step 2: 图片筛选与验证(CRITICAL)

⚠️ 这是最核心的步骤 - 每张图片都必须验证

对每张候选图片:

  1. 使用 Read 工具查看图片内容(必须执行)
  2. 评估图片质量
    • ✅ 保留:清晰、主题明确、有视觉冲击力
    • ✅ 保留:产品截图、架构图、数据可视化、代码示例
    • ✅ 保留:人物特写、场景照、技术演示
    • ❌ 丢弃:模糊、低质量、无关内容
    • ❌ 丢弃:纯文字图、广告、页面截图(带导航栏等)
    • ❌ 丢弃:重复或相似度过高的图片
  3. 记录保留原因:为什么这张图片值得发布?

筛选目标

  • 最终选出 3-8 张最优质的图片
  • 如果原始图片不足 3 张,全部使用(但要确保质量)
  • 如果原始图片超过 8 张,优先选择视觉冲击力最强的

图片优先级排序

  1. 🏆 高优先级:产品主视觉、架构图、关键数据图表
  2. 🥈 中优先级:功能演示、代码示例、对比图
  3. 🥉 低优先级:装饰性图片、通用配图

Step 3: 撰写口语化文案

文案要求

  • 长度:1-2 句话(20-50 字)
  • 风格:口语化、有真实感、像朋友圈的评论
  • 情感:可以是感叹、推荐、评论、疑问、期待

文案类型(选择最合适的一种)

类型 1:直接推荐

  • "这个 AI 工具真的很实用,解决了不少实际问题。"
  • "新发现的开源项目,值得一试。"
  • "看完这个技术分析,思路清晰了很多。"

类型 2:感叹式

  • "没想到这个功能这么强大。"
  • "这个设计思路很巧妙啊。"
  • "原来还可以这样用。"

类型 3:疑问式

  • "你们试过这个工具吗?"
  • "有人用过类似的方案吗?"
  • "这个方向会是未来趋势吗?"

类型 4:期待式

  • "期待这个项目的后续发展。"
  • "看好这个技术方向。"
  • "值得持续关注。"

❌ 避免

  • 过于正式的书面语("该工具具有...")
  • 夸张的营销语气("震撼发布"、"颠覆性")
  • 机械化的模板句式

Step 4: 生成话题标签

标签要求

  • 数量:3-5 个标签
  • 格式#标签1 #标签2 #标签3(标签之间用空格分隔)
  • 内容:与图片主题紧密相关

标签类型(组合使用)

核心主题标签(必选 1-2 个):

  • 技术类:#AI #机器学习 #深度学习 #大模型 #Agent
  • 产品类:#ChatGPT #Claude #Copilot #Cursor
  • 工具类:#开发工具 #效率工具 #AI工具

细分领域标签(可选 1-2 个):

  • #Prompt工程 #RAG #LangChain #AutoGPT
  • #代码生成 #自动化测试 #技术架构
  • #开源项目 #技术分享 #开发经验

情感/行动标签(可选 1 个):

  • #效率神器 #推荐 #值得一试
  • #技术洞察 #干货分享 #学习笔记

标签选择原则

  • ✅ 精准:标签要准确反映图片内容
  • ✅ 热门:优先使用高搜索量的标签(如 #AI #ChatGPT)
  • ✅ 层次:结合宽泛标签(#AI)和细分标签(#Prompt工程)
  • ❌ 避免:无关标签、过长的标签、生僻标签

Step 5: 发布图片消息

使用工具mcp__wenyan-mcp__publish_image_message

参数说明

  • title:主标题(从文章标题提取,或根据主题自拟,10-20字)
  • content:文案正文(Step 3 的口语化文案 + Step 4 的话题标签)
  • image_paths:图片路径数组(Step 2 筛选后的 3-8 张图片,按优先级排序)
  • need_open_comment:是否开启评论(默认 true)
  • only_fans_can_comment:是否仅粉丝评论(默认 false)

Read the full file on GitHub · 274 lines

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. 8d ago First seen · 274 lines · 54 tokens per session scan A fab101cf17ab

Subscribe to this mod's changes

ai-tech-editor is an agent published in the GitHub repository floodsung/gongzhonghao_agent_team (63 stars, last pushed 7mo ago), licensed MIT. It adds 54 tokens to every session and 2,713 once invoked, about $0.0003 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 2026-09-01.

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