wechat-article-evaluator

wechat-article-evaluator is a skill for Claude Code from HeroAshacker/wechat-content-pipeline. It costs 137 tokens per session (4,961 once invoked), scanned A, original, MIT.

An evaluator for Chinese WeChat public-account articles, scoring them across five areas on a 100-point scale. WeChat public accounts are channels used to publish articles to followers.

In plain words
What is it for?
Use it to assess an article's title, emotional appeal, structure, case detail, and likelihood of being widely shared, using pasted text or a Markdown or text file.
Why use it?
It turns a general review into a structured assessment with a total score, quality level, and specific suggestions. It also supports a pass-or-fail quality check for publishing workflows.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to assess an article's title, emotional appeal, structure, case detail, and likelihood of being widely shared, using pasted text or a Markdown or text file.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/heroashacker/wechat-content-pipeline/wechat-article-evaluator
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 HeroAshacker/wechat-content-pipeline --skill wechat-article-evaluator
Clone the repo
git clone --depth 1 https://github.com/HeroAshacker/wechat-content-pipeline

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 wechat-article-evaluator

README.md
[![agentmods](https://agentmods.dev/badge/skills/heroashacker/wechat-content-pipeline/wechat-article-evaluator.svg)](https://agentmods.dev/skills/heroashacker/wechat-content-pipeline/wechat-article-evaluator)
Your own site
<a href="https://agentmods.dev/skills/heroashacker/wechat-content-pipeline/wechat-article-evaluator"><img src="https://agentmods.dev/badge/skills/heroashacker/wechat-content-pipeline/wechat-article-evaluator.svg" alt="Measured on agentmods" height="20"></a>
Per session 137 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,961 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 $0.00137 $0.04961
Opus 5 $0.00068 $0.02481
Sonnet 5 $0.00027 $0.00992
Haiku 4.5 $0.00014 $0.00496

Measured 8d ago against content hash 86c7a4046e8f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

wechat-article-evaluator 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.

skills/wechat-article-evaluator/SKILL.md · 405 lines

How it starts

The opening of the file, as written. The whole thing — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.

微信公众号文章质量评估器 (wechat-article-evaluator)

触发条件

  • 评估文章, 文章打分, 文章质量评估
  • 公众号文章评分, 爆文评估
  • /wechat-article-evaluator <文件路径>
  • 这篇文章能爆吗, 文章能打多少分

Instructions

Step 1: 获取文章内容

  • 文件路径: .md.txt 文件 → 使用 Read 工具读取
  • 内联文本: 用户直接粘贴的文章内容
  • 无输入: 提示用户提供文章内容或文件路径

Step 2: 预分析

快速扫描文章基本信息:

检查项 说明
标题 提取文章标题(首行 # 或第一行)
字数 统计全文字数
段落数 统计段落结构
目标受众 根据内容推断目标读者群

Step 3: 五维度评分

逐一评估以下 5 个维度,每个维度 20 分,总分 100 分。 详细评分标准见下方「评估维度」章节。

Step 4: 生成评估报告

按照「输出格式」章节的模板输出完整报告,包含:

  • 总分与等级
  • 各维度详细评分(含进度条)
  • 综合评价
  • 具体改进建议(至少 3 条可操作建议)

质量等级

等级 分数 说明
A+ 90-100 爆文潜质
A 80-89 优质文章
B 70-79 合格文章
C 60-69 需要改进
D 50-59 较差
F <50 需要重写

评估维度

总分 100 分,5 个维度

维度 满分 核心关注
标题吸引力 20 记忆锚点、好奇心触发、简洁有力
情绪共鸣 20 真实感、打动人、代入感
结构完整性 20 逻辑通顺、节奏合理、首尾呼应
案例深度 20 具体细节、说服力、真实经历
爆文潜力 20 传播性、分享欲、受众匹配

维度 1: 标题吸引力 (20分)

子项 分值 评分标准
记忆锚点 7 标题是否包含具体数字/人物/场景,让人过目不忘。7=画面感极强,4=有亮点,2=平淡
好奇心触发 7 是否制造信息差/悬念/反常识。7=非点不可,4=有点想看,2=无感
简洁有力 6 字数15-25字为佳,无废字,节奏感好。6=精炼有力,3=略冗长,1=拖沓或过短

维度 2: 情绪共鸣 (20分)

子项 分值 评分标准
真实感 7 是否有真实的情感表达,非套路化鸡汤。7=真诚动人,4=尚可,2=假大空
打动力 7 能否触发读者情绪(感动/愤怒/共鸣/启发)。7=强烈共鸣,4=有触动,2=无感
代入感 6 读者能否把自己代入场景。6=身临其境,3=部分代入,1=旁观者视角

维度 3: 结构完整性 (20分)

子项 分值 评分标准
逻辑通顺 7 段落间因果/递进/转折是否自然。7=行云流水,4=基本通顺,2=跳跃混乱
节奏合理 7 长短段交替、张弛有度、不拖沓。7=节奏感强,4=尚可,2=平铺直叙
首尾呼应 6 开头吸引+结尾升华,形成闭环。6=完美闭环,3=有头有尾,1=虎头蛇尾

维度 4: 案例深度 (20分)

子项 分值 评分标准
具体细节 7 案例是否有时间/地点/人物/对话等细节。7=细节丰满,4=有细节,2=笼统空泛
说服力 7 案例能否有力支撑观点。7=无可辩驳,4=有说服力,2=牵强附会
真实经历 6 是否来自真实经历或可信来源。6=亲身经历,3=二手可信,1=编造感强

维度 5: 爆文潜力 (20分)

子项 分值 评分标准
传播性 7 内容是否自带传播属性(争议/共鸣/实用)。7=必转,4=想转,2=不想转
分享欲 7 读者转发时能否获得社交货币。7=转发涨身份,4=值得分享,2=无分享动力
受众匹配 6 内容与目标受众的匹配度。6=精准命中,3=部分匹配,1=受众模糊

Read the full file on GitHub · 405 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 · 405 lines · 137 tokens per session scan A 86c7a4046e8f

Subscribe to this mod's changes

wechat-article-evaluator is a skill published in the GitHub repository HeroAshacker/wechat-content-pipeline (11 stars, last pushed 6mo ago), licensed MIT. It adds 137 tokens to every session and 4,961 once invoked, about $0.0007 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens