Write Blog Post logo

Write Blog Post

Organization
techwolf-ai
write-blog-post

Write or develop a blog post. Use for blog content - writing, drafting, developing ideas into drafts, or editing. Longer-form than LinkedIn (800-1200 words) with section structure.

Overview

Publishertechwolf-ai
Repositoryai-first-toolkit
Skill namewrite-blog-post
Stars
138
Forks
7
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by techwolf-ai on GitHub. Read the source before you install it.

Installation

Install the Write Blog Post 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/techwolf-ai/ai-first-toolkit/tree/main/plugins/content-studio/skills/write-blog-post
  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/techwolf-ai/ai-first-toolkit.git /tmp/ai-first-toolkit
mkdir -p .claude/skills
cp -r /tmp/ai-first-toolkit/plugins/content-studio/skills/write-blog-post .claude/skills/write-blog-post
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Write Blog Post 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 Write Blog Post 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 Write Blog Post 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.

Write Blog Post

You are helping write a blog post for the author.

Before Writing (MANDATORY)

You MUST complete these steps before writing ANY content:

  1. Run ./scripts/print-published.sh linkedin-post to read ALL published LinkedIn posts
    • Blog posts share the same voice as LinkedIn posts, so these are your primary style reference
    • Note core insights and data points already used
    • Identify opportunities to reference or build on previous posts
  2. Run ./scripts/print-published.sh blog-post to read ALL published blog posts
    • Avoid repeating topics, angles, or arguments already covered
    • Note the structure and depth of existing blog posts
  3. Read guidelines/linkedin.md for style rules (same voice applies to blog posts)
  4. Read references/professional-profile.md for background

If developing an idea-stage post: Check if the idea's core insight overlaps with published posts (LinkedIn or blog). If so, either:

  • Find a genuinely different angle
  • Explicitly build on the previous piece
  • Recommend against developing the idea

Avoid Repetitive Patterns

When reading recent posts, actively note and vary:

Openings: Vary between demand signal, personal anecdote, company experience, surprising data Section flow: Don't always follow the same arc (problem-solution-data-close) Closing lines: Find fresh ways to land the argument Examples: Rotate between company-specific, industry, and broader examples Rhetorical devices: If recent pieces use lists heavily, try flowing narrative, and vice versa

The goal is a consistent voice with varied execution.

Style Requirements

  • 800-1200 words (use wc -w to verify)
  • English language
  • Section headings (## level) to break up the piece into 5-7 sections
  • First person throughout
  • Conversational tone - read it aloud, does it sound like something you'd say?
  • Specific > abstract - lead with concrete examples, generalize later
  • No hype words ("revolutionary", "game-changing", "transformative")
  • Strong opening - first 2-3 sentences must hook the reader with something concrete

Structure

Blog posts are longer than LinkedIn posts and need more structure. A typical blog post has:

  1. Hook (80-100 words) - Concrete opening that pulls the reader in
  2. 3-5 body sections (150-200 words each) - Each with a clear heading and one main point
  3. Close (50-75 words) - Echo the opening or land the core argument

Each section should earn its place. If a section doesn't add something the reader couldn't get from the LinkedIn post on the same topic, cut it.

Relationship to LinkedIn Posts

Blog posts often expand on ideas first introduced in LinkedIn posts. When this happens:

  • Reference the LinkedIn post briefly, don't re-explain it
  • Go deeper on the angle the LinkedIn post couldn't cover in 200 words
  • Add the meta-story, the data, or the "why" behind the insight
  • Blog posts should reward readers who already saw the LinkedIn post AND stand alone for those who didn't

Process

  1. If given a topic, develop it into a full draft
  2. If given an existing idea file (search with ./scripts/search-posts.sh), develop the content
  3. Always check word count against 800-1200 target
  4. Save to content/posts/blog-post/ with type: blog-post, stage: 02-drafts

Validation

After saving the draft, run the word count checker:

bash
scripts/check-word-count.sh <yaml-file>

Adjust the content if the word count is outside the 800-1200 target.

Sub-Agent Review

Before presenting the final draft, spawn a sub-agent to review with fresh eyes. The reviewer should:

  • Check voice consistency against guidelines/linkedin.md (same voice applies)
  • Verify each section earns its place and adds depth beyond a LinkedIn post
  • Flag any overlap with published posts (LinkedIn or blog)
  • Check for hype words, generic phrasing, or sections that could be cut

Incorporate the reviewer's feedback before finalizing.

Creating New Files

Get timestamp first:

bash
date -u +"%Y%m%d-%H%M%S"  # For slug
date -u +"%Y-%m-%dT%H:%M:%S.000Z"  # For created/lastUpdated

File path: content/posts/blog-post/{slug}-{slugified-title}.yaml

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Write Blog Post AI skill do?

Write or develop a blog post. Use for blog content - writing, drafting, developing ideas into drafts, or editing. Longer-form than LinkedIn (800-1200 words) with section structure.

Why use Write Blog Post on TypingMind?

Because you install it once and use it with any model. Write Blog Post 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 Write Blog Post in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/content-studio/skills/write-blog-post. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Write Blog Post?

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 Write Blog Post?

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

Is the Write Blog Post AI skill free?

Yes. It is published on GitHub by techwolf-ai under the MIT license. 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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