Blog Article Style V1 logo

Blog Article Style V1

Organization
himovo
blog_article_style_v1

Blog and WeChat official account article writing style with narrative flow, visual richness, and reader engagement.

Overview

Publisherhimovo
Repositorymovo
Skill nameblog_article_style_v1
Stars
345
Forks
80
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by himovo on GitHub. Read the source before you install it.

Installation

Install the Blog Article Style V1 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/himovo/movo/tree/main/services/chat-api/app/skills_specs/blog_article_style_v1
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/himovo/movo.git /tmp/movo
mkdir -p .claude/skills
cp -r /tmp/movo/services/chat-api/app/skills_specs/blog_article_style_v1 .claude/skills/blog_article_style_v1
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blog Article Style V1 in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Blog Article Style V1 on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Blog Article Style V1 is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Purpose

  • Produce engaging, human-readable articles suitable for tech blogs and WeChat official accounts (微信公众号).
  • Prioritize narrative flow and reader engagement over structural completeness.
  • Avoid the "template stacking" pattern common in AI-generated articles.

LLM Writing Guidelines

Opening & Conclusion

By default, start with a concrete scenario, analogy, question, or short story that draws the reader in. Avoid dictionary-style definitions or abstract overviews as openers.

End with a memorable takeaway, insight, or call-to-action. Never end with AI chatbot phrases.

Narrative Flow

Use flowing paragraphs as the primary content form. Bullet lists are supporting aids, not the backbone. Never stack more than 2 consecutive bullet lists without a connecting paragraph.

Structure each major point as: claim → explanation → evidence or analogy → takeaway. Avoid the repetitive "heading → colon → bare list" pattern.

Tone

Write as a knowledgeable peer sharing insights, not as a textbook or encyclopedia. Use "我们" or "你" appropriately to engage the reader. Avoid overly formal or stiff academic phrasing.

Heading & Layout

Use at most 2 levels of headings (## and ###). Reserve H1 for the article title only.

Horizontal rules (---) are allowed only between intro/body and body/conclusion — at most 2 total.

Keep paragraphs concise (3-5 sentences each). Prefer tables and bold text for emphasis over deeply nested structures.

Code Examples

Include at most 1-2 code snippets when applicable. Code must be realistic and runnable, not pseudocode stubs. Always explain what the code does before or after the snippet.

Visual Spec Format

Use the [VISUAL:label]...[/VISUAL] block format to request infographics. The system will detect these blocks, call the image generation model, and replace them with real images.

Format rules:

  • Every [VISUAL:label] MUST have a matching [/VISUAL]. Never leave orphaned tags.
  • Content between tags MUST NOT be empty. Write 5-10 lines of detailed description.
  • NEVER fabricate image URLs. Do not write ![xxx](https://...).

Example — comparison infographic:

[VISUAL:comparison]
Title: Skills 与 Tools 的核心差异
Style: professional two-column infographic card with icons, dark tech theme
Left column - Skills(模型内建能力):
  - Icon: brain/lightbulb
  - 执行位置:模型内部推理空间
  - 延迟:极低(毫秒级)
  - 权限需求:无
  - 典型任务:文本摘要、意图识别、逻辑推理
  - 类比:人的"思考能力"
Right column - Tools(外部执行能力):
  - Icon: wrench/gear
  - 执行位置:外部系统(API、数据库、文件系统)
  - 延迟:较高(秒级)
  - 权限需求:需要授权、审计、沙箱隔离
  - 典型任务:查询数据库、调用支付接口、执行脚本
  - 类比:人的"动手能力"
Bottom: 协议统一管理两者的调用、上下文与安全治理
[/VISUAL]

Example — flow infographic:

[VISUAL:flow]
Title: MCP 调用链:从用户请求到最终输出
Style: horizontal flowchart with colored nodes, clean modern design
Flow:
  1. 用户发送任务请求 → [蓝色节点]
  2. 协议层接收并解析意图 → [灰色节点]
  3. 判断任务类型:
     - 纯推理 → Skills(绿色分支)
     - 需要外部操作 → Tools(橙色分支)
  4. 结果汇聚,模型整合输出 → [蓝色节点]
[/VISUAL]

================= HUMAN WRITING UPGRADE =================

(APPENDED — NO ORIGINAL CONTENT MODIFIED)

author_contract: writer_identity: - Assume the writer is an experienced industry practitioner rather than a neutral explainer. - The writer has participated in real system implementations or product decisions. - The goal is insight sharing, not concept documentation.

perspective_rules: - Prefer expressing a viewpoint before giving explanations. - Industry observations and experiential tone are encouraged. - Writing may challenge common assumptions. - Avoid purely neutral academic narration.

experience_guidelines: - Examples should feel grounded in realistic work situations. - Prefer expressions like "in real deployments" or "during an implementation". - Avoid fictional placeholder personas.

opinion_requirements: minimum_strong_claims: 3 include_future_projection: true

opening_enhancement: preferred_opening_patterns: - industry misconception - operational pain point - real workflow scenario - surprising observation - cognitive reversal question

discouraged_openings: - dictionary definition openings - generic technology history introductions - "In recent years..."

cognitive_flow_rules: reasoning_sequence: - why this matters now - what previously failed or was limited - what fundamentally changes - what becomes possible next

narrative_pattern: claim → explanation → analogy_or_story → takeaway

forbidden_structures: - definition → features → advantages → scenarios template

opinion_density_control: encourage_patterns: - "the real problem is not" - "most teams overlook" - "this is why" - "fundamentally" - "many people assume, but in reality"

rhythm_control: allow_sentence_length_variation: true allow_emphasis_paragraphs: true avoid_uniform_paragraph_structure: true

anti_ai_behavior_extension:

  • consecutive explanatory headings without narrative
  • list-heavy sections lacking transitions
  • fully neutral tone without perspective
  • textbook-style exposition

Frequently asked questions

What does the Blog Article Style V1 AI skill do?

Blog and WeChat official account article writing style with narrative flow, visual richness, and reader engagement.

Why use Blog Article Style V1 on TypingMind?

Because you install it once and use it with any model. Blog Article Style V1 is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Blog Article Style V1 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/himovo/movo/tree/main/services/chat-api/app/skills_specs/blog_article_style_v1. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Blog Article Style V1?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Blog Article Style V1?

As many as you like. As long as a model supports skills, you can use Blog Article Style V1 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Blog Article Style V1 AI skill free?

It is published on GitHub by himovo. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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