Technical Blog Writing logo

Technical Blog Writing

Organization
101-skills
technical-blog-writing

Technical blog post writing with structure, code examples, and developer audience conventions. Covers post types, code formatting, explanation depth, and developer-specific engagement patterns. Use for: engineering blogs, dev tutorials, technical writing, developer content, documentation posts. Triggers: technical blog, dev blog, engineering blog, technical writing, developer tutorial, tech post, code tutorial, programming blog, developer content, technical article, engineering post, coding tutorial, technical content

Overview

Publisher101-skills
Repositorysuperpowers
Skill nametechnical-blog-writing
Stars
5
Forks
3
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 101-skills on GitHub. Read the source before you install it.

Installation

Install the Technical Blog Writing 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/101-skills/superpowers/tree/main/guides/writing/technical-blog-writing
  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/101-skills/superpowers.git /tmp/superpowers
mkdir -p .claude/skills
cp -r /tmp/superpowers/guides/writing/technical-blog-writing .claude/skills/technical-blog-writing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Technical Blog Writing 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 Technical Blog Writing 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 Technical Blog Writing 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.

Install the belt CLI skill: npx skills add belt-sh/cli

Technical Blog Writing

Write developer-focused technical blog posts via inference.sh CLI.

Quick Start

Requires inference.sh CLI (belt). Install instructions

bash
belt login

# Research topic depth
belt app run exa/search --input '{
  "query": "building REST API Node.js best practices 2024 tutorial"
}'

# Generate header image
belt app run infsh/html-to-image --input '{
  "html": "<div style=\"width:1200px;height:630px;background:linear-gradient(135deg,#0f172a,#1e293b);display:flex;align-items:center;padding:60px;font-family:ui-monospace,monospace;color:white\"><div><p style=\"font-size:18px;color:#38bdf8;margin:0\">// engineering blog</p><h1 style=\"font-size:48px;margin:16px 0;font-weight:800;font-family:system-ui;line-height:1.2\">How We Reduced API Latency by 90% with Edge Caching</h1><p style=\"font-size:20px;opacity:0.6;font-family:system-ui\">A deep dive into our CDN architecture</p></div></div>"
}'

Post Types

1. Tutorial / How-To

Step-by-step instruction. The reader should be able to follow along and build something.

Structure:
1. What we're building (with screenshot/demo)
2. Prerequisites
3. Step 1: Setup
4. Step 2: Core implementation
5. Step 3: ...
6. Complete code (GitHub link)
7. Next steps / extensions
RuleWhy
Show the end result firstReader knows if it's worth continuing
List prerequisites explicitlyDon't waste time of wrong audience
Every code block should be runnableCopy-paste-run is the test
Explain the "why" not just the "how"Tutorials that explain reasoning get shared
Include error handlingReal code has errors
Link to complete code repoReference after tutorial

2. Deep Dive / Explainer

Explains a concept, technology, or architecture decision in depth.

Structure:
1. What is [concept] and why should you care?
2. How it works (simplified mental model)
3. How it works (detailed mechanics)
4. Real-world example
5. Trade-offs and when NOT to use it
6. Further reading

3. Postmortem / Incident Report

Describes what went wrong, why, and what was fixed.

Structure:
1. Summary (what happened, impact, duration)
2. Timeline of events
3. Root cause analysis
4. Fix implemented
5. What we're doing to prevent recurrence
6. Lessons learned

4. Benchmark / Comparison

Data-driven comparison of tools, approaches, or architectures.

Structure:
1. What we compared and why
2. Methodology (so results are reproducible)
3. Results with charts/tables
4. Analysis (what the numbers mean)
5. Recommendation (with caveats)
6. Raw data / reproducibility instructions

5. Architecture / System Design

Explains how a system is built and why decisions were made.

Structure:
1. Problem we needed to solve
2. Constraints and requirements
3. Options considered
4. Architecture chosen (with diagram)
5. Trade-offs we accepted
6. Results and lessons

Writing Rules for Developers

Voice and Tone

DoDon't
Be direct: "Use connection pooling""You might want to consider using..."
Admit trade-offs: "This adds complexity"Pretend your solution is perfect
Use "we" for team decisions"I single-handedly architected..."
Specific numbers: "reduced p99 from 800ms to 90ms""significantly improved performance"
Cite sources and benchmarksMake unsourced claims
Acknowledge alternativesPretend yours is the only way

What Developers Hate

❌ "In today's fast-paced world of technology..." (filler)
❌ "As we all know..." (if we all know, why are you writing it?)
❌ "Simply do X" (nothing is simple if you're reading a tutorial)
❌ "It's easy to..." (dismissive of reader's experience)
❌ "Obviously..." (if it's obvious, don't write it)
❌ Marketing language in technical content
❌ Burying the lede under 3 paragraphs of context

Code Examples

RuleWhy
Every code block must be runnableBroken examples destroy trust
Show complete, working examplesSnippets without context are useless
Include language identifier in fenced blocksSyntax highlighting
Show output/result after codeReader verifies understanding
Use realistic variable namescalculateTotalRevenue not foo
Include error handling in examplesReal code handles errors
Pin dependency versions"Works with React 18.2" not "React"
Good code block format:

```python
# What this code does (one line)
def calculate_retry_delay(attempt: int, base_delay: float = 1.0) -> float:
    """Exponential backoff with jitter."""
    delay = base_delay * (2 ** attempt)
    jitter = random.uniform(0, delay * 0.1)
    return delay + jitter

# Usage
delay = calculate_retry_delay(attempt=3)  # ~8.0-8.8 seconds

### Explanation Depth

| Audience Signal | Depth |
|----------------|-------|
| "Getting started with X" | Explain everything, assume no prior knowledge |
| "Advanced X patterns" | Skip basics, go deep on nuances |
| "X vs Y" | Assume familiarity with both, focus on differences |
| "How we built X" | Technical audience, can skip fundamentals |

**State your assumed audience level explicitly** at the start:

"This post assumes familiarity with Docker and basic Kubernetes concepts. If you're new to containers, start with [our intro post]."


## Blog Post Structure

### The Ideal Structure

```markdown
# Title (contains primary keyword, states outcome)

[Hero image or diagram]

**TL;DR:** [2-3 sentence summary with key takeaway]

## The Problem / Why This Matters
[Set up why the reader should care — specific, not generic]

## The Solution / How We Did It
[Core content — code, architecture, explanation]

### Step 1: [First thing]
[Explanation + code + output]

### Step 2: [Second thing]
[Explanation + code + output]

## Results
[Numbers, benchmarks, outcomes — be specific]

## Trade-offs and Limitations
[Honest about downsides — builds trust]

## Conclusion
[Key takeaway + what to do next]

## Further Reading
[3-5 relevant links]

Word Count by Type

TypeWord CountWhy
Quick tip500-800One concept, one example
Tutorial1,500-3,000Step-by-step needs detail
Deep dive2,000-4,000Thorough exploration
Architecture post2,000-3,500Diagrams carry some load
Benchmark1,500-2,500Data and charts do heavy lifting

Diagrams and Visuals

When to Use Diagrams

ScenarioDiagram Type
Request flowSequence diagram
System architectureBox-and-arrow diagram
Decision logicFlowchart
Data modelER diagram
Performance comparisonBar/line chart
Before/afterSide-by-side
bash
# Generate architecture diagram
belt app run infsh/html-to-image --input '{
  "html": "<div style=\"width:1200px;height:600px;background:#0f172a;display:flex;align-items:center;justify-content:center;padding:40px;font-family:system-ui;color:white\"><div style=\"display:flex;gap:40px;align-items:center\"><div style=\"background:#1e293b;border:2px solid #334155;border-radius:8px;padding:24px;text-align:center;width:160px\"><p style=\"font-size:14px;color:#94a3b8;margin:0\">Client</p><p style=\"font-size:18px;font-weight:bold;margin:8px 0 0\">React App</p></div><div style=\"color:#64748b;font-size:32px\">→</div><div style=\"background:#1e293b;border:2px solid #3b82f6;border-radius:8px;padding:24px;text-align:center;width:160px\"><p style=\"font-size:14px;color:#94a3b8;margin:0\">Edge</p><p style=\"font-size:18px;font-weight:bold;margin:8px 0 0\">CDN Cache</p></div><div style=\"color:#64748b;font-size:32px\">→</div><div style=\"background:#1e293b;border:2px solid #334155;border-radius:8px;padding:24px;text-align:center;width:160px\"><p style=\"font-size:14px;color:#94a3b8;margin:0\">API</p><p style=\"font-size:18px;font-weight:bold;margin:8px 0 0\">Node.js</p></div><div style=\"color:#64748b;font-size:32px\">→</div><div style=\"background:#1e293b;border:2px solid #334155;border-radius:8px;padding:24px;text-align:center;width:160px\"><p style=\"font-size:14px;color:#94a3b8;margin:0\">Database</p><p style=\"font-size:18px;font-weight:bold;margin:8px 0 0\">PostgreSQL</p></div></div></div>"
}'

# Generate benchmark chart
belt app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfig, ax = plt.subplots(figsize=(12, 6))\nfig.patch.set_facecolor(\"#0f172a\")\nax.set_facecolor(\"#0f172a\")\n\ntools = [\"Express\", \"Fastify\", \"Hono\", \"Elysia\"]\nrps = [15000, 45000, 62000, 78000]\ncolors = [\"#64748b\", \"#64748b\", \"#3b82f6\", \"#64748b\"]\n\nax.barh(tools, rps, color=colors, height=0.5)\nfor i, v in enumerate(rps):\n    ax.text(v + 1000, i, f\"{v:,} req/s\", va=\"center\", color=\"white\", fontsize=14)\n\nax.set_xlabel(\"Requests per second\", color=\"white\", fontsize=14)\nax.set_title(\"HTTP Framework Benchmark (Hello World)\", color=\"white\", fontsize=18, fontweight=\"bold\")\nax.tick_params(colors=\"white\", labelsize=12)\nax.spines[\"top\"].set_visible(False)\nax.spines[\"right\"].set_visible(False)\nax.spines[\"bottom\"].set_color(\"#334155\")\nax.spines[\"left\"].set_color(\"#334155\")\nplt.tight_layout()\nplt.savefig(\"benchmark.png\", dpi=150, facecolor=\"#0f172a\")\nprint(\"Saved\")"
}'

Distribution

Where Developers Read

PlatformFormatHow to Post
Your blogFull articlePrimary — own your content
Dev.toCross-post (canonical URL back to yours)Markdown import
HashnodeCross-post (canonical URL)Markdown import
Hacker NewsLink submissionShow HN for projects, tell HN for stories
Reddit (r/programming, r/webdev, etc.)Link or discussionFollow subreddit rules
Twitter/XThread summary + linkSee twitter-thread-creation skill
LinkedInAdapted version + linkSee linkedin-content skill
bash
# Cross-post thread to X
belt app run x/post-create --input '{
  "text": "New blog post: How We Reduced API Latency by 90%\n\nThe short version:\n→ Moved computation to edge\n→ Aggressive cache-control headers\n→ Eliminated N+1 queries\n\np99 went from 800ms to 90ms.\n\nFull deep dive with code: [link]"
}'

Common Mistakes

MistakeProblemFix
No TL;DRBusy devs leave before getting the point2-3 sentence summary at the top
Broken code examplesDestroys all credibilityTest every code block before publishing
No version pinningCode breaks in 6 months"Works with Node 20, React 18.2"
"Simply do X"Dismissive, condescendingRemove "simply", "just", "easily"
No diagrams for architectureWalls of text describing systemsOne diagram > 500 words of description
Marketing toneDevelopers instantly disengageDirect, technical, honest
No trade-offs sectionReads as biased marketingAlways discuss downsides
Giant introduction before contentReaders bounceGet to the point in 2-3 paragraphs
Unpinned dependenciesTutorial breaks for future readersPin versions, note date written
No "Further Reading"Dead end, no context3-5 links to deepen understanding

Related Skills

bash
npx skills add inference-sh/skills@seo-content-brief
npx skills add inference-sh/skills@content-repurposing
npx skills add inference-sh/skills@og-image-design

Browse all apps: belt app list

Frequently asked questions

What does the Technical Blog Writing AI skill do?

Technical blog post writing with structure, code examples, and developer audience conventions. Covers post types, code formatting, explanation depth, and developer-specific engagement patterns. Use for: engineering blogs, dev tutorials, technical writing, developer content, documentation posts. Triggers: technical blog, dev blog, engineering blog, technical writing, developer tutorial, tech post, code tutorial, programming blog, developer content, technical article, engineering post, coding tutorial, technical content

Why use Technical Blog Writing on TypingMind?

Because you install it once and use it with any model. Technical Blog Writing 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 Technical Blog Writing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/101-skills/superpowers/tree/main/guides/writing/technical-blog-writing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Technical Blog Writing?

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 Technical Blog Writing?

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

Is the Technical Blog Writing AI skill free?

It is published on GitHub by 101-skills. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇